From 77908883dc336389b35d4c9e9a60d042c57095af Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Thu, 1 Aug 2024 10:47:53 +0700 Subject: [PATCH 01/32] . --- .../Nested_CV/NestedCV.ipynb | 30 +++++++++++++------ Performance-Estimation/Nested_CV/NestedCV2.py | 2 +- 2 files changed, 22 insertions(+), 10 deletions(-) diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb index 611c21b..3292239 100644 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ b/Performance-Estimation/Nested_CV/NestedCV.ipynb @@ -17,14 +17,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy: 0.97\n" + "Accuracy: 0.97\n", + "Root Mean Squared Error (RMSE): 0.16\n" ] } ], @@ -32,6 +33,8 @@ "from sklearn.datasets import load_iris\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import mean_squared_error\n", + "import numpy as np\n", "\n", "iris = load_iris()\n", "\n", @@ -46,7 +49,14 @@ "y_pred = RandForest.predict(X)\n", "acc = accuracy_score(y, y_pred)\n", "\n", - "print(f'Accuracy: {acc:.2f}')" + "print(f'Accuracy: {acc:.2f}')\n", + "\n", + "# Calculate the Mean Squared Error (MSE)\n", + "mse = mean_squared_error(y, y_pred)\n", + "\n", + "# The sacred RMSE emerges!\n", + "rmse = np.sqrt(mse)\n", + "print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')" ] }, { @@ -67,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -80,6 +90,8 @@ ], "source": [ "from sklearn.model_selection import cross_validate\n", + "from sklearn.metrics import mean_squared_error\n", + "import numpy as np\n", "\n", "def do_cross_validation(model, print_model = False):\n", " fold = 5\n", @@ -87,10 +99,10 @@ " scores = ' + '.join(f'{s:.2f}' for s in cv[\"test_score\"])\n", " mean_ = cv[\"test_score\"].mean()\n", " msg = f'Cross-validated accuracy: ({scores}) / {fold} = {mean_:.2f}'\n", - " \n", + "\n", " if print_model:\n", " msg = f'{model}:\\n\\t{msg}\\n'\n", - " \n", + "\n", " print(msg)\n", "\n", "do_cross_validation(RandForest)" @@ -110,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -151,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -176,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": {}, "outputs": [ { diff --git a/Performance-Estimation/Nested_CV/NestedCV2.py b/Performance-Estimation/Nested_CV/NestedCV2.py index 1d15849..daf27b1 100644 --- a/Performance-Estimation/Nested_CV/NestedCV2.py +++ b/Performance-Estimation/Nested_CV/NestedCV2.py @@ -5,7 +5,7 @@ from sklearn.svm import SVC # Number of random trials -NUM_TRIALS = 30 +NUM_TRIALS = 20 # Load the dataset iris = load_iris() From 2fd590520004138038654aa841f5fe460b75b4b6 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Thu, 1 Aug 2024 13:51:01 +0700 Subject: [PATCH 02/32] New nested exp --- .../Nested_CV/NestedCV.ipynb | 223 +++++++----------- 1 file changed, 91 insertions(+), 132 deletions(-) diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb index a5d2a5e..fc34b53 100644 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ b/Performance-Estimation/Nested_CV/NestedCV.ipynb @@ -4,183 +4,142 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nested CV VS Normal CV" + "

Nested Cross Validation

\n", + "

Normal Cross-validation Problem

\n", + "Overoptimistic Performance Estimate\n", + "\n", + "1. Dataset used to select bast hyperparameter.\n", + "2. Lead to over optimistic estimation.\n", + "3. because, model is evaluated in data it has already seen during hyperparamater turning.\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 58, "metadata": {}, + "outputs": [], "source": [ - "Linear With resubstitution" + "import pandas as pd\n", + "\n", + "# Load the dataset\n", + "data = pd.read_csv('HeightWeight.csv')\n", + "\n", + "# Extract features (X) and target variable (y)\n", + "X = data['Height'].values.reshape(-1, 1) # Reshape to 2D array\n", + "y = data['Weight'].values\n" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy: 0.97\n" + "Resubstitution RMSE: 5.542246180431454\n", + "CV RMSE: 5.65\n" ] } ], "source": [ - "from sklearn.datasets import load_iris\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "iris = load_iris()\n", + "import numpy as np\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", "\n", - "X = iris.data\n", - "y = iris.target\n", + "# Resubstitution\n", + "model = LinearRegression()\n", + "model.fit(X, y)\n", + "y_pred = model.predict(X)\n", + "resubstitution_rmse = np.sqrt(mean_squared_error(y, y_pred))\n", "\n", - "RandForest = RandomForestClassifier(n_estimators=2, random_state=0)\n", - "# X is our training data\n", - "RandForest.fit(X, y)\n", + "from sklearn.model_selection import cross_val_score\n", "\n", - "# This is an overly optimistic estimation since we are using X again!\n", - "y_pred = RandForest.predict(X)\n", - "acc = accuracy_score(y, y_pred)\n", + "# Cross-validation\n", + "fold = 10\n", + "model = LinearRegression()\n", + "cv_scores = cross_val_score(model, X, y, cv=fold, scoring='neg_mean_squared_error')\n", + "cv_rmse = np.sqrt(-cv_scores).mean()\n", "\n", - "print(f'Accuracy: {acc:.2f}')" + "#output\n", + "print(f\"Resubstitution RMSE: {resubstitution_rmse}\")\n", + "print(f\"CV RMSE: {cv_rmse:.2f}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For test and train => shouldn't be train and test with same data.\n", - "Use Cross validate to split data into many fold.\n", - "Then test each fold with model from remaining data set.\n", - "\n", - "/++Train++/++Train++/++Train++/**Test**/ => acc \n", - "/++Train++/++Train++/**Test**/++Train++/ => acc \n", - "/++Train++/**Test**/++Train++/++Train++/ => acc \n", - "/**Test**/++Train++/++Train++/++Train++/ => acc\n", - "\n", - "CV acc = mean of acc in each fold" + "

Compare Normal CV and Nested CV

" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cross-validated accuracy: (0.97 + 0.97 + 0.93 + 0.90 + 1.00) / 5 = 0.95\n" + "Mean RMSE (nested CV): 5.5428\n", + "Mean RMSE (regular CV): 5.7660\n", + "Mean RMSE (resubstution): 5.5422\n" ] } ], "source": [ - "from sklearn.model_selection import cross_validate\n", - "\n", - "def do_cross_validation(model, print_model = False):\n", - " fold = 5\n", - " cv = cross_validate(model, X, y, scoring='accuracy', cv=fold)\n", - " scores = ' + '.join(f'{s:.2f}' for s in cv[\"test_score\"])\n", - " mean_ = cv[\"test_score\"].mean()\n", - " msg = f'Cross-validated accuracy: ({scores}) / {fold} = {mean_:.2f}'\n", - " \n", - " if print_model:\n", - " msg = f'{model}:\\n\\t{msg}\\n'\n", - " \n", - " print(msg)\n", - "\n", - "do_cross_validation(RandForest)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SVM moodel (Support Vector Machine)\n", - "Model for classification, classificate by hyperplane and find best option\n", - "Parameter for SVM\n", - "parameter C: Increase size of vector line, if it too much => will over-fit\n", - "Kernels: for solve non-linear problem by using 3d \n", - "https://medium.com/@pradyasin/support-vector-machines-svm-943f9a732a69" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cross-validated accuracy: (0.97 + 0.97 + 0.97 + 0.93 + 1.00) / 5 = 0.97\n" - ] - } - ], - "source": [ - "from sklearn.svm import SVC\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import cross_val_score, KFold\n", + "from sklearn.linear_model import LinearRegression \n", + "from sklearn.metrics import mean_squared_error\n", "\n", - "svc = SVC(random_state=0)\n", - "do_cross_validation(svc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From better result in SVM, Let continue with it.\n", - "///////////////////////////////////////////////\n", - "Nested CV\n", - "\n", - "/++Train++/++Train++/++Train++/**Test**/ => acc \n", - "/++Train++/++Train++/**Test**/++Train++/ => acc \n", - "/++Train++/**Test**/++Train++/++Train++/ => acc \n", - "/**Test**/++Train++/++Train++/++Train++/ => acc <-----------------------\\\n", - " |-----------------------------| |\n", - " Use Training set to do CV |\n", - " |\n", - " /**Evaluate**/++Train++/++Train++/ => acc --| |\n", - " /++Train++/**Evaluate**/++Train++/ => acc |=> select best one |\n", - " /++Train++/++Train++/**Evaluate**/ => acc --| from each estimators\n", - " and re-train" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GridSearchCV(estimator=SVC(random_state=0),\n", - " param_grid={'kernel': ['linear', 'poly']}):\n", - "\tCross-validated accuracy: (0.97 + 1.00 + 0.97 + 0.97 + 1.00) / 5 = 0.98\n", - "\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", + "model = LinearRegression()\n", "\n", - "# svc inner loop\n", - "svc_grid = GridSearchCV(SVC(random_state=0), param_grid={'kernel': ['linear', 'poly']})\n", - "# svc outer loop\n", - "do_cross_validation(svc_grid, print_model=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "do_cross_validation(SVC(kernel='linear', random_state=0), print_model=True)\n", - "do_cross_validation(SVC(kernel='poly', random_state=0), print_model=True)" + "outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)\n", + "\n", + "rmse_scores = []\n", + "\n", + "# Outer loop: split data into train and validation sets\n", + "for train_idx, val_idx in outer_cv.split(X):\n", + " X_train, X_val = X[train_idx], X[val_idx]\n", + " y_train, y_val = y[train_idx], y[val_idx]\n", + "\n", + " # Define the inner loop (for model evaluation)\n", + " inner_cv = KFold(n_splits=3, shuffle=True, random_state=42)\n", + " inner_rmse_scores = []\n", + "\n", + " # Inner loop: perform k-fold cross-validation on the training set\n", + " for train_idx_inner, val_idx_inner in inner_cv.split(X_train):\n", + " X_train_inner, X_val_inner = X_train[train_idx_inner], X_train[val_idx_inner]\n", + " y_train_inner, y_val_inner = y_train[train_idx_inner], y_train[val_idx_inner]\n", + " model.fit(X_train_inner, y_train_inner)\n", + " y_pred_inner = model.predict(X_val_inner)\n", + " inner_rmse = np.sqrt(mean_squared_error(y_val_inner, y_pred_inner))\n", + " inner_rmse_scores.append(inner_rmse)\n", + "\n", + " # Calculate the mean RMSE across inner folds\n", + " outer_rmse = np.mean(inner_rmse_scores)\n", + " rmse_scores.append(outer_rmse)\n", + "\n", + "\n", + "# Calculate the mean RMSE across outer folds\n", + "mean_rmse = np.mean(rmse_scores)\n", + "print(f\"Mean RMSE (nested CV): {mean_rmse:.4f}\")\n", + "\n", + "cv_scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')\n", + "mean_cv_rmse = np.sqrt(-np.mean(cv_scores))\n", + "print(f\"Mean RMSE (regular CV): {mean_cv_rmse:.4f}\")\n", + "\n", + "#compare with resubstution\n", + "model.fit(X, y)\n", + "y_pred = model.predict(X)\n", + "resubstitution_rmse = np.sqrt(mean_squared_error(y, y_pred))\n", + "print(f\"Mean RMSE (resubstution): {resubstitution_rmse:.4f}\")\n", + "\n", + "\n" ] } ], From 3a4f8dfa3ba834dabd79d00da3f85bc636a8be07 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Thu, 1 Aug 2024 13:53:13 +0700 Subject: [PATCH 03/32] merge --- Performance-Estimation/Nested_CV/NestedCV.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb index fc34b53..ed6d79a 100644 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ b/Performance-Estimation/Nested_CV/NestedCV.ipynb @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -91,7 +91,6 @@ ], "source": [ "import numpy as np\n", - "import pandas as pd\n", "from sklearn.model_selection import cross_val_score, KFold\n", "from sklearn.linear_model import LinearRegression \n", "from sklearn.metrics import mean_squared_error\n", From 45c08979f2ce3206a6dd9800a9ee8c2302acc1ae Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Thu, 1 Aug 2024 13:53:46 +0700 Subject: [PATCH 04/32] rm --- Performance-Estimation/Nested_CV/ReadMe.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/Performance-Estimation/Nested_CV/ReadMe.md b/Performance-Estimation/Nested_CV/ReadMe.md index 5149d22..0e3292a 100644 --- a/Performance-Estimation/Nested_CV/ReadMe.md +++ b/Performance-Estimation/Nested_CV/ReadMe.md @@ -1,3 +1 @@ -# Nested Cross Validation 🪐 - -> we'll use NestedCV2. +Use jupyternotebook \ No newline at end of file From f6a37143f5a20801214c44201a45414a12d6812b Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Thu, 1 Aug 2024 13:55:01 +0700 Subject: [PATCH 05/32] add data --- .../Nested_CV/HeightWeight.csv | 10001 ++++++++++++++++ .../Nested_CV/NestedCV.ipynb | 25 +- 2 files changed, 10022 insertions(+), 4 deletions(-) create mode 100644 Performance-Estimation/Nested_CV/HeightWeight.csv diff --git a/Performance-Estimation/Nested_CV/HeightWeight.csv b/Performance-Estimation/Nested_CV/HeightWeight.csv new file mode 100644 index 0000000..0054002 --- /dev/null +++ b/Performance-Estimation/Nested_CV/HeightWeight.csv @@ -0,0 +1,10001 @@ +Height,Weight +187.5714232,109.7209851 +174.7060363,73.62273185 +188.2396677,96.49755015 +182.1966851,99.80950419 +177.4997615,93.59861876 +170.8226598,69.04221615 +174.7141064,83.42821885 +173.6052294,76.19035195 +170.2281321,79.80018673 +161.1794947,70.94164201 +180.836271,84.64250139 +181.967645,96.95128455 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+154.0604822,55.83005064 +154.2720578,54.85129299 +166.1006241,64.87174599 +168.2798846,56.25444212 +156.1487969,55.06027903 +163.8168874,67.76779995 +145.7344267,51.7966723 +157.6222702,56.76064174 +153.5995454,50.24358232 +153.5258903,61.4886549 +177.4660192,80.7357771 +167.2100433,60.24966772 +149.9794525,50.66956891 +172.8243054,77.34388273 +160.9158518,64.36765762 +166.6500179,68.56926453 +151.2283704,54.99570558 +154.825914,43.40270998 +160.4759256,64.07717264 +159.0971543,46.65355345 +157.6776925,62.90943664 +152.4773018,44.31023777 +150.1095551,50.13538119 +168.0785365,62.041159 +170.3505728,77.50431517 +162.2247002,58.2753768 +175.3469776,74.32216565 +157.3383845,51.55032378 \ No newline at end of file diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb index ed6d79a..6fab434 100644 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ b/Performance-Estimation/Nested_CV/NestedCV.ipynb @@ -15,9 +15,26 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'HeightWeight.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[66], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Load the dataset\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mHeightWeight.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# Extract features (X) and target variable (y)\u001b[39;00m\n\u001b[0;32m 7\u001b[0m X \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHeight\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# Reshape to 2D array\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1884\u001b[0m \u001b[43m 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\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'HeightWeight.csv'" + ] + } + ], "source": [ "import pandas as pd\n", "\n", @@ -31,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -76,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, "outputs": [ { From 7da5252e835e6ed827ab087d4d0b7732778c6095 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Thu, 1 Aug 2024 13:56:08 +0700 Subject: [PATCH 06/32] add noisy 80 sample --- .../Nested_CV/sin_noisy_80sample.csv | 81 +++++++++++++++++++ 1 file changed, 81 insertions(+) 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[PATCH 07/32] add more data sample --- .../Nested_CV/NestedCV.ipynb | 59 +++++++++---------- 1 file changed, 27 insertions(+), 32 deletions(-) diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb index 6fab434..8b3f324 100644 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ b/Performance-Estimation/Nested_CV/NestedCV.ipynb @@ -15,48 +15,38 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 71, "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'HeightWeight.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[66], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Load the dataset\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mHeightWeight.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# Extract features (X) and target variable (y)\u001b[39;00m\n\u001b[0;32m 7\u001b[0m X \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHeight\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# Reshape to 2D array\u001b[39;00m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'HeightWeight.csv'" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", "# Load the dataset\n", - "data = pd.read_csv('HeightWeight.csv')\n", + "#data = pd.read_csv('HeightWeight.csv')\n", + "\n", + "data = pd.read_csv('sin_noisy_80sample.csv')\n", "\n", "# Extract features (X) and target variable (y)\n", - "X = data['Height'].values.reshape(-1, 1) # Reshape to 2D array\n", - "y = data['Weight'].values\n" + "#X = data['Height'].values.reshape(-1, 1) # Reshape to 2D array\n", + "#y = data['Weight'].values\n", + "\n", + "X = data[['x', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8']]\n", + "X = X.to_numpy()\n", + "y = data['noisy_y'].values\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Resubstitution RMSE: 5.542246180431454\n", - "CV RMSE: 5.65\n" + "Resubstitution RMSE: 0.257766277865693\n", + "CV RMSE: 0.40\n" ] } ], @@ -93,16 +83,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean RMSE (nested CV): 5.5428\n", - "Mean RMSE (regular CV): 5.7660\n", - "Mean RMSE (resubstution): 5.5422\n" + "ename": "KeyError", + "evalue": "\"None of [Index([ 1, 2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 19, 20, 21, 23,\\n 24, 25, 26, 27, 29, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\\n 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\\n 69, 71, 72, 73, 74, 75, 76, 77, 78, 79],\\n dtype='int32')] are in the [columns]\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[73], line 14\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Outer loop: split data into train and validation sets\u001b[39;00m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train_idx, val_idx \u001b[38;5;129;01min\u001b[39;00m outer_cv\u001b[38;5;241m.\u001b[39msplit(X):\n\u001b[1;32m---> 14\u001b[0m X_train, X_val \u001b[38;5;241m=\u001b[39m \u001b[43mX\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtrain_idx\u001b[49m\u001b[43m]\u001b[49m, X[val_idx]\n\u001b[0;32m 15\u001b[0m y_train, y_val \u001b[38;5;241m=\u001b[39m y[train_idx], y[val_idx]\n\u001b[0;32m 17\u001b[0m \u001b[38;5;66;03m# Define the inner loop (for model evaluation)\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m 4107\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 4108\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m 4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m 6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6198\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m 6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m 6204\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\indexes\\base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m 6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[0;32m 6248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m-> 6249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6251\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m 6252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mKeyError\u001b[0m: \"None of [Index([ 1, 2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 19, 20, 21, 23,\\n 24, 25, 26, 27, 29, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\\n 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\\n 69, 71, 72, 73, 74, 75, 76, 77, 78, 79],\\n dtype='int32')] are in the [columns]\"" ] } ], From ff06aabff54341b0f766cee0a9558a68af9fbf4d Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Thu, 1 Aug 2024 14:19:59 +0700 Subject: [PATCH 08/32] . --- .../Nested_CV/NestedCV.ipynb | 23 ++++++++----------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb index 8b3f324..59379c3 100644 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ b/Performance-Estimation/Nested_CV/NestedCV.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -83,21 +83,16 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 76, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"None of [Index([ 1, 2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 19, 20, 21, 23,\\n 24, 25, 26, 27, 29, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\\n 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\\n 69, 71, 72, 73, 74, 75, 76, 77, 78, 79],\\n dtype='int32')] are in the [columns]\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[73], line 14\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Outer loop: split data into train and validation sets\u001b[39;00m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train_idx, val_idx \u001b[38;5;129;01min\u001b[39;00m outer_cv\u001b[38;5;241m.\u001b[39msplit(X):\n\u001b[1;32m---> 14\u001b[0m X_train, X_val \u001b[38;5;241m=\u001b[39m \u001b[43mX\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtrain_idx\u001b[49m\u001b[43m]\u001b[49m, X[val_idx]\n\u001b[0;32m 15\u001b[0m y_train, y_val \u001b[38;5;241m=\u001b[39m y[train_idx], y[val_idx]\n\u001b[0;32m 17\u001b[0m \u001b[38;5;66;03m# Define the inner loop (for model evaluation)\u001b[39;00m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m 4107\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 4108\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m 4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m 6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6198\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m 6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m 6204\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\indexes\\base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m 6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[0;32m 6248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m-> 6249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6251\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m 6252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mKeyError\u001b[0m: \"None of [Index([ 1, 2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 19, 20, 21, 23,\\n 24, 25, 26, 27, 29, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\\n 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\\n 69, 71, 72, 73, 74, 75, 76, 77, 78, 79],\\n dtype='int32')] are in the [columns]\"" + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean RMSE (nested CV): 0.3088\n", + "Mean RMSE (regular CV): 15.1747\n", + "Mean RMSE (resubstution): 0.2578\n" ] } ], From 1c40874f7289e52e2862cbc7fead7f2ea5efd91f Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Sat, 3 Aug 2024 12:08:10 +0700 Subject: [PATCH 09/32] Sync fork --- .../Nested-CrossValidation/NestedCV.ipynb | 6 +- .../Nested_CV/NestedCV.ipynb | 25 +- .../Regularization/ridge.ipynb | 255 ++++++++++++++++++ 3 files changed, 279 insertions(+), 7 deletions(-) create mode 100644 Performance-Estimation/Regularization/ridge.ipynb diff --git a/Performance-Estimation/Nested-CrossValidation/NestedCV.ipynb b/Performance-Estimation/Nested-CrossValidation/NestedCV.ipynb index 59379c3..e41dbce 100644 --- a/Performance-Estimation/Nested-CrossValidation/NestedCV.ipynb +++ b/Performance-Estimation/Nested-CrossValidation/NestedCV.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 3, "metadata": {}, "outputs": [ { diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb index 59379c3..4240be0 100644 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ b/Performance-Estimation/Nested_CV/NestedCV.ipynb @@ -15,9 +15,26 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'sin_noisy_80sample.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 6\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Load the dataset\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m#data = pd.read_csv('HeightWeight.csv')\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msin_noisy_80sample.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# Extract features (X) and target variable (y)\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m#X = data['Height'].values.reshape(-1, 1) # Reshape to 2D array\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m#y = data['Weight'].values\u001b[39;00m\n\u001b[0;32m 12\u001b[0m X \u001b[38;5;241m=\u001b[39m data[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^2\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^3\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^4\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^5\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^6\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^7\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^8\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1887\u001b[0m \u001b[43m 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whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'sin_noisy_80sample.csv'" + ] + } + ], "source": [ "import pandas as pd\n", "\n", @@ -38,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -83,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/Performance-Estimation/Regularization/ridge.ipynb b/Performance-Estimation/Regularization/ridge.ipynb new file mode 100644 index 0000000..1a6ec18 --- /dev/null +++ b/Performance-Estimation/Regularization/ridge.ipynb @@ -0,0 +1,255 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5.1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "def load_data(filename):\n", + " \"\"\"Loads data from a CSV file and splits it into features and target.\"\"\"\n", + " data = pd.read_csv(filename)\n", + " X = data['Height'].values.reshape(-1, 1) # Reshape for sklearn\n", + " y = data['Weight'].values\n", + " return X, y\n", + "\n", + "# Load data\n", + "X, y = load_data('HeightWeight.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def ridge_regression_and_plot(X, y, lambdas):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " for lambda_ in enumerate(lambdas):\n", + " ridge_model = Ridge(alpha=lambda_)\n", + " ridge_model.fit(X, y)\n", + " y_pred = ridge_model.predict(X)\n", + "\n", + " plt.plot(X, y_pred, label=f'lambda={lambda_}')\n", + " print(f\"Slope for lambda={lambda_}: {ridge_model.coef_[0]}\")\n", + "\n", + " plt.scatter(X, y, color='black', label='Data')\n", + " plt.xlabel('Height')\n", + " plt.ylabel('Weight')\n", + " plt.legend()\n", + " plt.title('Ridge Regression Models')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slope for lambda=100: 1.3780052819518878\n", + "Slope for lambda=10000: 1.363867725088684\n", + "Slope for lambda=1000000: 0.673201234065182\n", + "Slope for lambda=100000000: 0.0130363580588361\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def main():\n", + " filename = 'HeightWeight.csv'\n", + " X, y = load_data(filename)\n", + " lambdas = [10**2, 10**4, 10**6, 10**8] # Adjust lambda values as needed\n", + " slope_values = np.linspace(-2, 2, 100)\n", + " ridge_regression_and_plot(X, y, lambdas)\n", + "\n", + "if __name__ == '__main__':\n", + " main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "def predict(X, b0, b1):\n", + " return [b0 + b1 * x for x in X]\n", + "\n", + "plt.figure(figsize=(14, 10))\n", + "plt.subplot(2, 3, 1)\n", + "plt.scatter(X, y, color='blue', edgecolor='k')\n", + "plt.title('Data')\n", + "\n", + "alphas = [0, 10, 20, 40, 400]\n", + "slope_values = np.linspace(-2, 2, 100)\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "for alpha in alphas:\n", + " cost_ridge = [(slope - 0.5) ** 2 + alpha * slope ** 2 for slope in slope_values]\n", + " plt.plot(slope_values, cost_ridge, label=f'λ = {alpha}')\n", + "\n", + "plt.title('Sum of Squared Residuals + λ * Slope^2')\n", + "plt.xlabel('Slope Values')\n", + "plt.ylabel('Cost')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5.2" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "# Sample data (replace with your data)\n", + "data = {'X1': [1, 2, 3, 4, 5],\n", + " 'X2': [2, 4, 5, 4, 5],\n", + " 'y': [7, 10, 13, 12, 14]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Split data into features (X) and target variable (y)\n", + "X = df[['X1', 'X2']]\n", + "y = df['y']\n", + "\n", + "# Split data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Range of alpha values to explore\n", + "alpha_values = np.logspace(-4, 2, 100)\n", + "\n", + "# Initialize lists to store SSR values\n", + "train_error = []\n", + "test_error = []\n", + "\n", + "# Loop through alpha values\n", + "for alpha in alpha_values:\n", + " model = Ridge(alpha=alpha)\n", + " model.fit(X_train, y_train)\n", + "\n", + " # Calculate mean squared error (MSE) on training and test sets\n", + " train_pred = model.predict(X_train)\n", + " test_pred = model.predict(X_test)\n", + " train_error.append(mean_squared_error(y_train, train_pred))\n", + " test_error.append(mean_squared_error(y_test, test_pred))\n", + "\n", + "# Find the index of the minimum test error\n", + "best_alpha_index = np.argmin(test_error)\n", + "\n", + "# Plot the SSR curve\n", + "plt.plot(alpha_values, train_error, label='Training error')\n", + "plt.plot(alpha_values, test_error, label='Test error')\n", + "plt.axvline(x=alpha_values[best_alpha_index], color='red', linestyle='--', label='Best alpha')\n", + "plt.xscale('log')\n", + "plt.xlabel('Alpha')\n", + "plt.ylabel('Mean Squared Error (MSE)')\n", + "plt.title('Ridge Regression: MSE vs Alpha')\n", + "plt.legend()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 07e4c6b6c4b4d3b84af7d4efd0a9e9f6e99c4ef5 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Sat, 3 Aug 2024 13:09:22 +0700 Subject: [PATCH 10/32] Delete Unused folder --- .../Nested_CV/NestedCV.ipynb | 190 ------------------ 1 file changed, 190 deletions(-) delete mode 100644 Performance-Estimation/Nested_CV/NestedCV.ipynb diff --git a/Performance-Estimation/Nested_CV/NestedCV.ipynb b/Performance-Estimation/Nested_CV/NestedCV.ipynb deleted file mode 100644 index 4240be0..0000000 --- a/Performance-Estimation/Nested_CV/NestedCV.ipynb +++ /dev/null @@ -1,190 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Nested Cross Validation

\n", - "

Normal Cross-validation Problem

\n", - "Overoptimistic Performance Estimate\n", - "\n", - "1. Dataset used to select bast hyperparameter.\n", - "2. Lead to over optimistic estimation.\n", - "3. because, model is evaluated in data it has already seen during hyperparamater turning.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'sin_noisy_80sample.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[1], line 6\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Load the dataset\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m#data = pd.read_csv('HeightWeight.csv')\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msin_noisy_80sample.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# Extract features (X) and target variable (y)\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m#X = data['Height'].values.reshape(-1, 1) # Reshape to 2D array\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m#y = data['Weight'].values\u001b[39;00m\n\u001b[0;32m 12\u001b[0m X \u001b[38;5;241m=\u001b[39m data[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^2\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^3\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^4\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^5\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^6\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^7\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx^8\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'sin_noisy_80sample.csv'" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Load the dataset\n", - "#data = pd.read_csv('HeightWeight.csv')\n", - "\n", - "data = pd.read_csv('sin_noisy_80sample.csv')\n", - "\n", - "# Extract features (X) and target variable (y)\n", - "#X = data['Height'].values.reshape(-1, 1) # Reshape to 2D array\n", - "#y = data['Weight'].values\n", - "\n", - "X = data[['x', 'x^2', 'x^3', 'x^4', 'x^5', 'x^6', 'x^7', 'x^8']]\n", - "X = X.to_numpy()\n", - "y = data['noisy_y'].values\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Resubstitution RMSE: 0.257766277865693\n", - "CV RMSE: 0.40\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import mean_squared_error\n", - "\n", - "# Resubstitution\n", - "model = LinearRegression()\n", - "model.fit(X, y)\n", - "y_pred = model.predict(X)\n", - "resubstitution_rmse = np.sqrt(mean_squared_error(y, y_pred))\n", - "\n", - "from sklearn.model_selection import cross_val_score\n", - "\n", - "# Cross-validation\n", - "fold = 10\n", - "model = LinearRegression()\n", - "cv_scores = cross_val_score(model, X, y, cv=fold, scoring='neg_mean_squared_error')\n", - "cv_rmse = np.sqrt(-cv_scores).mean()\n", - "\n", - "#output\n", - "print(f\"Resubstitution RMSE: {resubstitution_rmse}\")\n", - "print(f\"CV RMSE: {cv_rmse:.2f}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Compare Normal CV and Nested CV

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean RMSE (nested CV): 0.3088\n", - "Mean RMSE (regular CV): 15.1747\n", - "Mean RMSE (resubstution): 0.2578\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from sklearn.model_selection import cross_val_score, KFold\n", - "from sklearn.linear_model import LinearRegression \n", - "from sklearn.metrics import mean_squared_error\n", - "\n", - "model = LinearRegression()\n", - "\n", - "outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)\n", - "\n", - "rmse_scores = []\n", - "\n", - "# Outer loop: split data into train and validation sets\n", - "for train_idx, val_idx in outer_cv.split(X):\n", - " X_train, X_val = X[train_idx], X[val_idx]\n", - " y_train, y_val = y[train_idx], y[val_idx]\n", - "\n", - " # Define the inner loop (for model evaluation)\n", - " inner_cv = KFold(n_splits=3, shuffle=True, random_state=42)\n", - " inner_rmse_scores = []\n", - "\n", - " # Inner loop: perform k-fold cross-validation on the training set\n", - " for train_idx_inner, val_idx_inner in inner_cv.split(X_train):\n", - " X_train_inner, X_val_inner = X_train[train_idx_inner], X_train[val_idx_inner]\n", - " y_train_inner, y_val_inner = y_train[train_idx_inner], y_train[val_idx_inner]\n", - " model.fit(X_train_inner, y_train_inner)\n", - " y_pred_inner = model.predict(X_val_inner)\n", - " inner_rmse = np.sqrt(mean_squared_error(y_val_inner, y_pred_inner))\n", - " inner_rmse_scores.append(inner_rmse)\n", - "\n", - " # Calculate the mean RMSE across inner folds\n", - " outer_rmse = np.mean(inner_rmse_scores)\n", - " rmse_scores.append(outer_rmse)\n", - "\n", - "\n", - "# Calculate the mean RMSE across outer folds\n", - "mean_rmse = np.mean(rmse_scores)\n", - "print(f\"Mean RMSE (nested CV): {mean_rmse:.4f}\")\n", - "\n", - "cv_scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')\n", - "mean_cv_rmse = np.sqrt(-np.mean(cv_scores))\n", - "print(f\"Mean RMSE (regular CV): {mean_cv_rmse:.4f}\")\n", - "\n", - "#compare with resubstution\n", - "model.fit(X, y)\n", - "y_pred = model.predict(X)\n", - "resubstitution_rmse = np.sqrt(mean_squared_error(y, y_pred))\n", - "print(f\"Mean RMSE (resubstution): {resubstitution_rmse:.4f}\")\n", - "\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 26470de7f28a8b0100e994b37e475af002b4b9c8 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Sat, 3 Aug 2024 13:18:23 +0700 Subject: [PATCH 11/32] Add ass2 py --- Regularization/scratching/Ridge_ass2.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Regularization/scratching/Ridge_ass2.py diff --git a/Regularization/scratching/Ridge_ass2.py b/Regularization/scratching/Ridge_ass2.py new file mode 100644 index 0000000..e69de29 From 8bae9c6c9741d284e1f6398f6664e95843c9a018 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Sat, 3 Aug 2024 16:49:16 +0700 Subject: [PATCH 12/32] ridge polynomial --- Regularization/scratching/Ridge_ass2.py | 71 +++++++++++++++++++++++++ 1 file changed, 71 insertions(+) diff --git a/Regularization/scratching/Ridge_ass2.py b/Regularization/scratching/Ridge_ass2.py index e69de29..f6c8979 100644 --- a/Regularization/scratching/Ridge_ass2.py +++ b/Regularization/scratching/Ridge_ass2.py @@ -0,0 +1,71 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import Ridge +from sklearn.metrics import mean_squared_error + +def polyRidge(degree): + # Create polynomial features + poly = PolynomialFeatures(degree) # Adjust degree as needed + X_poly = poly.fit_transform(X) + # Split data into training and testing sets + X_train, X_test, y_train, y_test = train_test_split(X_poly, y, test_size=0.2, random_state=42) + # Create and train the Ridge regression model + ridge = Ridge(alpha=100) # Adjust alpha for regularization strength + ridge.fit(X_train, y_train) + + # Make predictions on the test and training sets + y_pred_train = ridge.predict(X_train) + y_pred_test = ridge.predict(X_test) + + # Evaluate the model + # Calculate E-train and E-test (RMSE) + mse_train = mean_squared_error(y_train, y_pred_train) + mse_test = mean_squared_error(y_test, y_pred_test) + rmse_train = np.sqrt(mse_train) + rmse_test = np.sqrt(mse_test) + + # Print results and return errors + print(f"Degree={degree}: E_train: {rmse_train:.4f}, E_test: {rmse_test:.4f}") + return rmse_train, rmse_test + +def generate_sin(): + np.random.seed(42) + X = np.random.rand(100, 1) * 10 + y = np.sin(X) + np.random.randn(100) / 10 + return X, y + +def import_csv(path): + df = pd.read_csv(path) + X = df['Height'].values.reshape(-1, 1) # Reshape for sklearn + y = df['Weight'].values + return X, y + +X, y = import_csv("Regularization/dataset/HeightWeight.csv") + +# Define a range of degrees to explore +degrees = np.arange(1, 11) # Adjust the range as needed + +# Initialize lists to store errors +E_train_list = [] +E_test_list = [] + +# Call the function for each degree and store errors +for deg in degrees: + rmse_train, rmse_test = polyRidge(deg) + E_train_list.append(rmse_train) + E_test_list.append(rmse_test) + +# Plot the results +plt.figure(figsize=(8, 6)) +plt.plot(degrees, E_train_list, label="E_train", marker='o', linestyle='-') +plt.plot(degrees, E_test_list, label="E_test", marker='s', linestyle='-') +plt.xlabel("Model Complexity (Degree)") +plt.ylabel("RMSE") +plt.title("Model Complexity vs. E_train and E_test") +plt.legend() +plt.grid(True) +plt.tight_layout() +plt.show() \ No newline at end of file From bb188d7dcbfb66c5bcd0ea2ec0a80906cc730e3a Mon Sep 17 00:00:00 2001 From: Chonakan Chumtap Date: Sat, 3 Aug 2024 17:21:50 +0700 Subject: [PATCH 13/32] :ambulance: : added a bias line. --- LearningCurve/Noisy/LinearModel.ipynb | 92 +++++++++++---------- LearningCurve/Noisy/LinearOriginModel.ipynb | 90 ++++++++++---------- 2 files changed, 96 insertions(+), 86 deletions(-) diff --git a/LearningCurve/Noisy/LinearModel.ipynb b/LearningCurve/Noisy/LinearModel.ipynb index bc13d56..2ae108b 100644 --- a/LearningCurve/Noisy/LinearModel.ipynb +++ b/LearningCurve/Noisy/LinearModel.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -70,13 +70,13 @@ "(-2.0, 2.0)" ] }, - "execution_count": 3, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -116,62 +116,63 @@ "output_type": "stream", "text": [ "Sample Size: 2\n", - "Bias: 0.32940056561288517\n", - "Variance: 9.928758580539577\n", - "E_out: 10.348159146152463\n", + "Bias: 0.30431498476295016\n", + "Variance: 6.654415454949708\n", + "E_out: 7.048730439712658\n", "\n", "Sample Size: 10\n", - "Bias: 0.3312362759682788\n", - "Variance: 0.08174548608883046\n", - "E_out: 0.5029817620571093\n", + "Bias: 0.3009852888337678\n", + "Variance: 0.0832787023681219\n", + "E_out: 0.47426399120188967\n", "\n", "Sample Size: 20\n", - "Bias: 0.32811633536538976\n", - "Variance: 0.03516883344477911\n", - "E_out: 0.4532851688101689\n", + "Bias: 0.30008441934015195\n", + "Variance: 0.03708737623696172\n", + "E_out: 0.4271717955771137\n", "\n", "Sample Size: 30\n", - "Bias: 0.32807677208150837\n", - "Variance: 0.02318719995335271\n", - "E_out: 0.4412639720348611\n", + "Bias: 0.2996398879320528\n", + "Variance: 0.02382473546527945\n", + "E_out: 0.41346462339733225\n", "\n", "Sample Size: 40\n", - "Bias: 0.32742686103010193\n", - "Variance: 0.017153983089768204\n", - "E_out: 0.4345808441198701\n", + "Bias: 0.2994942653326635\n", + "Variance: 0.015847364917404877\n", + "E_out: 0.4053416302500683\n", "\n", "Sample Size: 50\n", - "Bias: 0.3270911945193211\n", - "Variance: 0.01315476308342712\n", - "E_out: 0.4302459576027482\n", + "Bias: 0.2995476341949623\n", + "Variance: 0.012252176888258672\n", + "E_out: 0.40179981108322094\n", "\n", "Sample Size: 60\n", - "Bias: 0.3269787875061509\n", - "Variance: 0.010693093728994189\n", - "E_out: 0.4276718812351451\n", + "Bias: 0.2993292052722416\n", + "Variance: 0.010888833754083593\n", + "E_out: 0.40021803902632525\n", "\n", "Sample Size: 70\n", - "Bias: 0.3268349658228573\n", - "Variance: 0.009157558905432921\n", - "E_out: 0.4259925247282902\n", + "Bias: 0.2993818292838258\n", + "Variance: 0.009669142139706238\n", + "E_out: 0.39905097142353196\n", "\n", "Sample Size: 80\n", - "Bias: 0.32656907792113193\n", - "Variance: 0.008399056829487616\n", - "E_out: 0.42496813475061956\n", + "Bias: 0.2993377144609506\n", + "Variance: 0.007783833902059132\n", + "E_out: 0.3971215483630097\n", "\n", "Sample Size: 90\n", - "Bias: 0.32698136592299804\n", - "Variance: 0.007077368253627459\n", - "E_out: 0.42405873417662554\n", + "Bias: 0.299421731321395\n", + "Variance: 0.007130906189551714\n", + "E_out: 0.3965526375109467\n", "\n", "Sample Size: 100\n", - "Bias: 0.32664994485183635\n", - "Variance: 0.006595551429726186\n", - "E_out: 0.4232454962815625\n", + "Bias: 0.29930950603501005\n", + "Variance: 0.006711054387490234\n", + "E_out: 0.39602056042250033\n", "\n", - "[0.00047399 0.23124511 0.25898581 0.27046051 0.273676 0.28126268\n", - " 0.28209611 0.28529381 0.2858847 0.28643742 0.28695819]\n", + "[1.96808462e-04 2.29917704e-01 2.59120081e-01 2.73001428e-01\n", + " 2.76424958e-01 2.78929651e-01 2.82232820e-01 2.86385562e-01\n", + " 2.84331336e-01 2.85955196e-01 2.86254097e-01]\n", "(11,)\n" ] } @@ -183,6 +184,7 @@ "E_in = [[] for _ in range(len(sample_size_sets))]\n", "E_d = [[] for _ in range(len(sample_size_sets))]\n", "E_out_all = []\n", + "bias_all = []\n", "\n", "\n", "for i in range(len(sample_size_sets)):\n", @@ -208,6 +210,7 @@ " z = np.square(mean_model - y)\n", " bias = np.mean(z)\n", " print(f\"Bias: {bias}\")\n", + " bias_all.append(bias)\n", " \n", " z = np.square(E_d[i] - mean_model)\n", " var_x = np.mean(z)\n", @@ -229,12 +232,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -245,8 +248,11 @@ ], "source": [ "plt.figure(figsize=(10, 6))\n", + "bias_avg = np.mean(bias_all)\n", + "plt.plot(sample_size_sets, bias_all, label=f'Bias {bias_avg:.2f}', c=\"black\", linestyle='--')\n", "plt.plot(sample_size_sets, E_in_all, label='E in', c=\"green\", linewidth=2.0)\n", "plt.plot(sample_size_sets, E_out_all, label='E out', c=\"red\", linewidth=2.0)\n", + "plt.ylim(0, 2)\n", "plt.xlabel('Sample Size')\n", "plt.ylabel('Error')\n", "plt.legend()\n", diff --git a/LearningCurve/Noisy/LinearOriginModel.ipynb b/LearningCurve/Noisy/LinearOriginModel.ipynb index 834568d..ea60403 100644 --- a/LearningCurve/Noisy/LinearOriginModel.ipynb +++ b/LearningCurve/Noisy/LinearOriginModel.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -70,13 +70,13 @@ "(-2.0, 2.0)" ] }, - "execution_count": 8, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -116,62 +116,62 @@ "output_type": "stream", "text": [ "Sample Size: 2\n", - "Bias: 0.3506579967811249\n", - "Variance: 0.35500964813189484\n", - "E_out: 0.7956676449130197\n", + "Bias: 0.38895088738864475\n", + "Variance: 0.36903124399102744\n", + "E_out: 0.8479821313796722\n", "\n", "Sample Size: 10\n", - "Bias: 0.3072662071030217\n", - "Variance: 0.04634279201821697\n", - "E_out: 0.44360899912123863\n", + "Bias: 0.3113943114540373\n", + "Variance: 0.04445648391073809\n", + "E_out: 0.44585079536477534\n", "\n", "Sample Size: 20\n", - "Bias: 0.3069814151033727\n", - "Variance: 0.018340029152929656\n", - "E_out: 0.41532144425630235\n", + "Bias: 0.30983260380476624\n", + "Variance: 0.019516979019040627\n", + "E_out: 0.41934958282380685\n", "\n", "Sample Size: 30\n", - "Bias: 0.3069965523966276\n", - "Variance: 0.012711143478384684\n", - "E_out: 0.4097076958750123\n", + "Bias: 0.30972808941777374\n", + "Variance: 0.01264475917511855\n", + "E_out: 0.41237284859289225\n", "\n", "Sample Size: 40\n", - "Bias: 0.3071375088778144\n", - "Variance: 0.008679437664795252\n", - "E_out: 0.4058169465426096\n", + "Bias: 0.3096662273132338\n", + "Variance: 0.009407724052277274\n", + "E_out: 0.40907395136551106\n", "\n", "Sample Size: 50\n", - "Bias: 0.3071967276579705\n", - "Variance: 0.007124966114389447\n", - "E_out: 0.40432169377235994\n", + "Bias: 0.3095439198885394\n", + "Variance: 0.008016849489075615\n", + "E_out: 0.407560769377615\n", "\n", "Sample Size: 60\n", - "Bias: 0.30726431819794553\n", - "Variance: 0.006297179747658597\n", - "E_out: 0.40356149794560414\n", + "Bias: 0.3095496945051691\n", + "Variance: 0.005896840123871307\n", + "E_out: 0.40544653462904046\n", "\n", "Sample Size: 70\n", - "Bias: 0.30730219656549784\n", - "Variance: 0.0052037562472336445\n", - "E_out: 0.4025059528127315\n", + "Bias: 0.3094780772491302\n", + "Variance: 0.004998496288463058\n", + "E_out: 0.40447657353759325\n", "\n", "Sample Size: 80\n", - "Bias: 0.30731294292946454\n", - "Variance: 0.004399132635541776\n", - "E_out: 0.4017120755650063\n", + "Bias: 0.3094602492073243\n", + "Variance: 0.004920977975920243\n", + "E_out: 0.40438122718324454\n", "\n", "Sample Size: 90\n", - "Bias: 0.30734857015106726\n", - "Variance: 0.00382336337525226\n", - "E_out: 0.4011719335263195\n", + "Bias: 0.309460450873244\n", + "Variance: 0.0042449580614523025\n", + "E_out: 0.4037054089346963\n", "\n", "Sample Size: 100\n", - "Bias: 0.3073222651582563\n", - "Variance: 0.0037116853050082445\n", - "E_out: 0.4010339504632645\n", + "Bias: 0.30943988954239243\n", + "Variance: 0.0035485810204735706\n", + "E_out: 0.402988470562866\n", "\n", - "[0.12390045 0.2553921 0.27546806 0.28202015 0.28141815 0.28674119\n", - " 0.28417011 0.28932915 0.28949661 0.29045158 0.28957264]\n", + "[0.12511467 0.2585506 0.27458946 0.28259022 0.27940388 0.28771811\n", + " 0.2880633 0.2892881 0.28957775 0.28935932 0.29021918]\n", "(11,)\n" ] } @@ -183,6 +183,7 @@ "E_in = [[] for _ in range(len(sample_size_sets))]\n", "E_d = [[] for _ in range(len(sample_size_sets))]\n", "E_out_all = []\n", + "bias_all = []\n", "\n", "\n", "for i in range(len(sample_size_sets)):\n", @@ -208,6 +209,7 @@ " z = np.square(mean_model - y)\n", " bias = np.mean(z)\n", " print(f\"Bias: {bias}\")\n", + " bias_all.append(bias)\n", " \n", " z = np.square(E_d[i] - mean_model)\n", " var_x = np.mean(z)\n", @@ -229,12 +231,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -245,6 +247,8 @@ ], "source": [ "plt.figure(figsize=(10, 6))\n", + "bias_avg = np.mean(bias_all)\n", + "plt.plot(sample_size_sets, bias_all, label=f'Bias {bias_avg:.2f}', c=\"black\", linestyle='--')\n", "plt.plot(sample_size_sets, E_in_all, label='E in', c=\"green\", linewidth=2.0)\n", "plt.plot(sample_size_sets, E_out_all, label='E out', c=\"red\", linewidth=2.0)\n", "plt.xlabel('Sample Size')\n", From c174283a3790acfb911f7dddd00d963e26e951d6 Mon Sep 17 00:00:00 2001 From: Chonakan Chumtap Date: Sat, 3 Aug 2024 16:48:12 +0700 Subject: [PATCH 14/32] :sparkles: : genaralization between none reg and reg. --- Linear-Regression/model/LinearRegression.py | 6 +- Regularization/scratching/5.1_vectorized.py | 2 +- Regularization/scratching/5.3_vectorized.py | 108 +++++++++++++++ .../scratching/model/LinearRegression.py | 127 ++++++++++++++++++ .../scratching/model/RidgeRegression.py | 15 +-- 5 files changed, 245 insertions(+), 13 deletions(-) create mode 100644 Regularization/scratching/5.3_vectorized.py create mode 100644 Regularization/scratching/model/LinearRegression.py diff --git a/Linear-Regression/model/LinearRegression.py b/Linear-Regression/model/LinearRegression.py index 2d8bf68..5045ab8 100644 --- a/Linear-Regression/model/LinearRegression.py +++ b/Linear-Regression/model/LinearRegression.py @@ -1,8 +1,8 @@ import numpy as np def gradientDescent(n_samples, lr, X, y, y_pred): - d_weigths = (1 / n_samples) * np.dot(X.T, (y_pred - y)) # d_weights = 1 / n * ∑(y_prediction - y_actual) * X - d_bias = (1 / n_samples) * np.sum(y_pred - y) # d_bias = 1 / n * ∑(y_prediction - y_actual) + d_weigths = np.mean(np.dot(X.T, (y_pred - y))) # d_weights = 1 / n * ∑(y_prediction - y_actual) * X + d_bias = np.mean((1 / n_samples) * np.sum(y_pred - y)) # d_bias = 1 / n * ∑(y_prediction - y_actual) weights_gradient = lr * d_weigths bias_gradient = lr * d_bias @@ -14,7 +14,7 @@ def normalEquation(X, y): return new_weights def costFunction(n_samples, y_pred, y): # Mean Sqaure Error (MSE) - return (1 / (2 * n_samples)) * np.sum((y_pred - y)**2) + return np.mean((y_pred - y) ** 2) class LinearRegression: diff --git a/Regularization/scratching/5.1_vectorized.py b/Regularization/scratching/5.1_vectorized.py index 4e1cb3e..83a6d9c 100644 --- a/Regularization/scratching/5.1_vectorized.py +++ b/Regularization/scratching/5.1_vectorized.py @@ -26,7 +26,7 @@ model.training(X_b, y, "gradientDes") # Make predictions - predictions = model.predict(X_b) + predictions = model.prediction(X_b) # Calculate and print the loss error = model.costFunction(X_b, y) diff --git a/Regularization/scratching/5.3_vectorized.py b/Regularization/scratching/5.3_vectorized.py new file mode 100644 index 0000000..65e37a2 --- /dev/null +++ b/Regularization/scratching/5.3_vectorized.py @@ -0,0 +1,108 @@ +import numpy as np +import matplotlib.pyplot as plt +from model.RidgeRegression import RidgeRegression +from model.LinearRegression import LinearRegression + +# Define X data (bias + weigths) +def define_X_include_bias(X): + X_b = np.c_[np.ones((len(X), 1)), X] + return X_b + +# Define the problem function +def problem(X): + return np.sin(np.dot(np.pi, X)) + +def meanModel(models): + return np.mean(models, axis=0) + +def computeBias(mean_model): + z = np.square(mean_model - y) + return np.mean(z) + +def computeVariance(E_d, mean_model): + z = np.square(E_d - mean_model) + var_x = np.mean(z) + return np.mean(var_x) + +def computeEout(bias, variance): + return bias + variance + +if __name__ == "__main__": + # Generate X features + X = np.linspace(-1, 1, 100) + # Compute y values + y = problem(X) + + # Define X data (bias + weigths) + X_b = define_X_include_bias(X) + + E_d_linear = [] + E_d_ridge = [] + # Initialize subplots + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) + + # Add the base plot to both subplots + ax1.plot(X, y, label='sin(pi x)') + ax1.set_xlim(-1, 1) + ax1.set_xlabel('x') + ax1.set_ylabel('sin(pi x)') + ax1.set_ylim(-2, 2) + + ax2.plot(X, y, label='sin(pi x)') + ax2.set_xlim(-1, 1) + ax2.set_xlabel('x') + ax2.set_ylabel('sin(pi x)') + ax2.set_ylim(-2, 2) + + # Perform the training and plotting in the loop + for _ in range(1000): + linearModel = LinearRegression() + ridgeModel = RidgeRegression(lambda_param=0.5) + + # Randomly select 2 values from the dataset + rands_X = np.random.choice(X, 2) + y_sample = problem(rands_X) + X_sample = define_X_include_bias(rands_X) + + linearModel.training(X=X_sample, y=y_sample, type="normalEq") + ridgeModel.training(X=X_sample, y=y_sample, type="normalEq") + + y_pred_lin = linearModel.prediction(X_b) + E_d_linear.append(y_pred_lin) + + y_pred_ridge = ridgeModel.prediction(X_b) + E_d_ridge.append(y_pred_ridge) + + ax1.plot(X, y_pred_lin, c="black", alpha=0.05) + ax2.plot(X, y_pred_ridge, c="black", alpha=0.05) + + mean_linearModel = meanModel(E_d_linear) + mean_ridgeModel = meanModel(E_d_ridge) + + bias_linearModel = computeBias(mean_linearModel) + variance_linearModel = computeVariance(E_d_linear, mean_linearModel) + eOut_linearModel = computeEout(bias_linearModel, variance_linearModel) + + bias_ridgeModel = computeBias(mean_ridgeModel) + variance_ridgeModel = computeVariance(E_d_ridge, mean_ridgeModel) + eOut_ridgeModel = computeEout(bias_ridgeModel, variance_ridgeModel) + + print("\nLinear regression") + print("Bias: ", bias_linearModel) + print("Variance: ", variance_linearModel) + print("E out: ", eOut_linearModel) + + print("\nRidge regression") + print("Bias: ", bias_ridgeModel) + print("Variance: ", variance_ridgeModel) + print("E out: ", eOut_ridgeModel) + + ax1.set_title(f'Linear Regression \n Bias = {bias_linearModel:.2f} | Varinace = {variance_linearModel:.2f} | E out = {eOut_linearModel:.2f}') + ax2.set_title(f'Ridge Regression \n Bias = {bias_ridgeModel:.2f} | Varinace = {variance_ridgeModel:.2f} | E out = {eOut_ridgeModel:.2f}') + + ax1.plot(X, mean_linearModel, c="red", label="Mean Model", linewidth=2) + ax2.plot(X, mean_ridgeModel, c="red", label="Mean Model", linewidth=2) + + # Display the plots + plt.tight_layout() + plt.show() \ No newline at end of file diff --git a/Regularization/scratching/model/LinearRegression.py b/Regularization/scratching/model/LinearRegression.py new file mode 100644 index 0000000..9d63149 --- /dev/null +++ b/Regularization/scratching/model/LinearRegression.py @@ -0,0 +1,127 @@ +import numpy as np + +def gradientDescent(n_samples, lr, X, y, y_pred): + d_weigths = np.mean(np.dot(X.T, (y_pred - y))) # d_weights = 1 / n * ∑(y_prediction - y_actual) * X + d_bias = np.mean((1 / n_samples) * np.sum(y_pred - y)) # d_bias = 1 / n * ∑(y_prediction - y_actual) + + weights_gradient = lr * d_weigths + bias_gradient = lr * d_bias + + return weights_gradient, bias_gradient + +def normalEquation(X, y): + new_weights = np.linalg.pinv(X.T.dot(X)).dot(X.T.dot(y)) # w = (XT * X)^-1 * (XT * y) + return new_weights + +def costFunction(n_samples, y_pred, y): # Mean Sqaure Error (MSE) + return np.mean((y_pred - y) ** 2) + +class LinearRegression: + + def __init__(self, lr = 0.001, n_iters = 0): + self.lr = lr + self.n_iters = n_iters + self.weights = 0 # wi + self.bias = 0 # w0 + self.n_samples = 0 + self.weights_history = [] + self.bias_history = [] + self.costs_history = [] + + def standardization(self, X): # X_std = X - mean of X / standard deviation of X + mean_x = np.array([np.mean(X)]) + std_x = np.array([np.std(X)]) + X_std = (X - mean_x) / std_x + return X_std + + # Evaluation + Optimization + def training(self, X, y, type): + if (type == "gradientDes"): + n_samples, n_features = X.shape # in the shape of n x m, which means n is number of samples and m means number of features + self.n_samples = n_samples # set default number of samples + self.weights = np.zeros(n_features) # wights will be zero for all feature at the first time and the size of array depends on the total of features + + for _ in range(self.n_iters): + y_pred = self.prediction(X) + weights_gradient, bias_gradient = gradientDescent(n_samples, self.lr, X, y, y_pred) + self.weights -= weights_gradient + self.bias -= bias_gradient + self.weights_history.append(self.weights[0]) + self.bias_history.append(self.bias) + self.costs_history.append(costFunction(self.n_samples, y_pred, y)); + elif (type == "normalEq"): + self.weights = normalEquation(X, y) + else: + raise Exception("Sorry, we don't have that type of optimization.") + + def training_forLR(self, X, y, lr): + n_samples, n_features = X.shape # in the shape of n x m, which means n is number of samples and m means number of features + self.n_samples = n_samples # set default number of samples + self.weights = np.zeros(n_features) # wights will be zero for all feature at the first time and the size of array depends on the total of features + + for _ in range(self.n_iters): + y_pred = self.prediction(X) + weights_gradient, bias_gradient = gradientDescent(n_samples, lr, X, y, y_pred) + self.weights -= weights_gradient + self.bias -= bias_gradient + self.weights_history.append(self.weights[0]) + self.bias_history.append(self.bias) + self.costs_history.append(costFunction(self.n_samples, y_pred, y)); + + def generate_costs_forContour(self, X, y, w_range, b_range): + W, B = np.meshgrid(w_range, b_range) + costs_history = np.zeros(W.shape) + + for i in range(len(w_range)): + for j in range(len(b_range)): + weights = w_range[i] + bias = b_range[j] + y_pred = weights * X[:, 0] + bias + costs_history[j, i] = costFunction(self.n_samples, y_pred, y) + + return W, B, costs_history + + def get_Weights_Bias_History(self): + return self.weights_history, self.bias_history + + def get_Costs_History(self): + return self.costs_history + + # Representation + def prediction(self, X): + # y = wx + b, in the form of matrix calculation y = (w * X) + b + return np.dot(X, self.weights) + self.bias + +class CustomLinearRegression: + def __init__(self, n_iters = 0): + self.n_iters = n_iters + self.weights = 0 + self.bias = 0 + self.weights_history = [] + self.bias_history = [] + self.costs_history = [] + + def training(self, X, y, lr): + n_samples, n_features = X.shape # in the shape of n x m, which means n is number of samples and m means number of features + self.weights = np.zeros(n_features) # wights will be zero for all feature at the first time and the size of array depends on the total of features + + for _ in range(self.n_iters): + y_pred = self.prediction(X, -2, 0) + weights_gradient, bias_gradient = gradientDescent(n_samples, lr, X, y, y_pred) + self.weights -= weights_gradient + self.bias -= bias_gradient + self.weights_history.append(self.weights[0]) + self.bias_history.append(self.bias) + self.costs_history.append(costFunction(n_samples, y_pred, y)); + + def prediction(self, X, weights, bias): + _, n_features = X.shape # in the shape of n x m, which means n is number of samples and m means number of features + weights = np.zeros(n_features) # wights will be zero for all feature at the first time and the size of array depends on the total of features + return np.dot(X, weights) + bias + + def costFunction(self, X, y_pred, y): + n_samples, n_features = X.shape # in the shape of n x m, which means n is number of samples and m means number of features + return (1 / (2 * n_samples)) * np.sum((y_pred - y)**2) + + def get_Value(self): + return self.weights_history, self.bias_history, self.costs_history \ No newline at end of file diff --git a/Regularization/scratching/model/RidgeRegression.py b/Regularization/scratching/model/RidgeRegression.py index 4585cd5..ed7f695 100644 --- a/Regularization/scratching/model/RidgeRegression.py +++ b/Regularization/scratching/model/RidgeRegression.py @@ -22,16 +22,16 @@ def __init__(self, lambda_param, weights = None): self.weights_history = [] self.costs_history = [] - def training(self, X, y, mode, lr = 0.1, n_iters = 100): - if (mode == "normalEq"): + def training(self, X, y, type, lr = 0.1, n_iters = 100): + if (type == "normalEq"): self.weights = normalEquation(X, y, self.lambda_param) - elif (mode == "gradientDes"): + elif (type == "gradientDes"): n_samples, n_features = X.shape self.weights = np.zeros(n_features) for _ in range(n_iters): self.weights_history.append(self.weights[1]) self.costs_history.append(self.costFunction(X, y)) - y_pred = self.predict(X) + y_pred = self.prediction(X) weights_gradient = gradientDescent(X, y, y_pred, n_samples, self.lambda_param, self.weights) self.weights -= lr * weights_gradient else: @@ -43,20 +43,17 @@ def standardization(self, X): # X_std = X - mean of X / standard deviation X_std = (X - mean_x) / std_x return X_std - def test(self): - print(self.weights.shape) - def get_Weights_History(self): return self.weights_history def get_Costs_History(self): return self.costs_history - def predict(self, X): + def prediction(self, X): return np.dot(X, self.weights) def costFunction(self, X, y): - y_pred = self.predict(X) + y_pred = self.prediction(X) mse = np.sum((y - y_pred) ** 2) regularization = self.lambda_param * np.sum(self.weights[1:] ** 2) return mse + regularization \ No newline at end of file From 232b495c1abfe1b21c2af35766dbe66f4e6935cc Mon Sep 17 00:00:00 2001 From: Chonakan Chumtap Date: Sat, 3 Aug 2024 17:45:07 +0700 Subject: [PATCH 15/32] :memo: : added comment to 5.1. --- Regularization/README.md | 1 + Regularization/scratching/5.1_vectorized.py | 54 +++++++++++-------- .../scratching/model/RidgeRegression.py | 7 ++- 3 files changed, 36 insertions(+), 26 deletions(-) create mode 100644 Regularization/README.md diff --git a/Regularization/README.md b/Regularization/README.md new file mode 100644 index 0000000..bde3de2 --- /dev/null +++ b/Regularization/README.md @@ -0,0 +1 @@ +# Regularization 🦺 diff --git a/Regularization/scratching/5.1_vectorized.py b/Regularization/scratching/5.1_vectorized.py index 83a6d9c..d71333c 100644 --- a/Regularization/scratching/5.1_vectorized.py +++ b/Regularization/scratching/5.1_vectorized.py @@ -3,68 +3,78 @@ import matplotlib.pyplot as plt from model.RidgeRegression import RidgeRegression -# Example usage: if __name__ == "__main__": - df = pd.read_csv('../dataset/HeightWeight.csv') - first_5_rows = df.head(100) - + + # df = pd.read_csv('../dataset/HeightWeight.csv') + # first_5_rows = df.head(100) # Sample data # X = first_5_rows["Height"].values # y = first_5_rows["Weight"].values + # Example data that easy to visualize the diff between slope of each lambda X = np.array([[0], [2]]) y = np.array([0, 2]) - # Create and train the model - model = RidgeRegression(lambda_param=0) + # Use Ridge regression with lambda equals to 0.5 + model = RidgeRegression(lambda_param=0.5) + # Standardize data to avoid loss value to be NaN X = model.standardization(X) - # Add bias term to X + # Add bias term to X at the first column X_b = np.c_[np.ones((X.shape[0], 1)), X] + # Train model with Gradient Descent model.training(X_b, y, "gradientDes") - # Make predictions - predictions = model.prediction(X_b) + # Make a prediction + y_pred = model.prediction(X_b) - # Calculate and print the loss - error = model.costFunction(X_b, y) - print("Loss:", error) + # Calculate and print the loss of the model + error = model.costFunction(X_b, y, y_pred) + print("Model Loss:", error) - # Plotting + # Plot prediction line plt.figure(figsize=(10, 6)) # Plot original data points plt.scatter(X, y, color='blue', label='Data points') - - plt.plot(X, predictions, color='red', label='Prediction line') - - # Labels and title + # Plot prediction line + plt.plot(X, y_pred, color='red', label='Prediction line') plt.xlabel('Height') plt.ylabel('Weight') plt.title('Ridge Regression') plt.legend() - lambdas = [0, 10, 20 , 40, 400] - slope_vals = np.arange(-30.1, 30.1, 0.1) - slopes = np.array([[0, v] for v in slope_vals]) + # Use 5 lambda examples to see the diff + lambdas = [0, 10, 20, 40, 400] + # Generate slope value from -10.1 to 10.1 + slope_vals = np.arange(-10.1, 10.1, 0.1) + + # Add bias term for every slopes (known as weights) + slopes = np.array([[1, v] for v in slope_vals]) + + # Plot RSS + lambda * slope (y axis) with slopes (x axis) plt.figure(figsize=(10, 6)) for i, lambda_param in enumerate(lambdas): w_history = [] c_history = [] for j, slope_param in enumerate(slopes): + # Use weights from weight genarator for visualization model = RidgeRegression(lambda_param=lambda_param, weights=slope_param) - loss = model.costFunction(X_b, y) + # Get cost function from every prediction from each slope and each lambda + y_pred = model.prediction(X_b) + loss = model.costFunction(X_b, y, y_pred) w_history.append(slope_param[1]) c_history.append(loss) + # Plot the bowl plot to see the optimum slope plt.plot(w_history, c_history, label=f"λ {lambda_param}") plt.ylim(0, 20) plt.xlim(-10, 12) plt.xlabel('Slope Value') - plt.ylabel('Sum of Squared Residuals + λ x |Slope|') + plt.ylabel('Sum of Squared Residuals + λ x Slope ^ 2') plt.title('Ridge Regression Visualization') plt.legend() diff --git a/Regularization/scratching/model/RidgeRegression.py b/Regularization/scratching/model/RidgeRegression.py index ed7f695..1c10e9b 100644 --- a/Regularization/scratching/model/RidgeRegression.py +++ b/Regularization/scratching/model/RidgeRegression.py @@ -1,7 +1,7 @@ import numpy as np def gradientDescent(X, y, y_pred, n_samples, lambda_param, weights): - gradient = (1 / n_samples) * (X.T @ (y_pred - y)) + (lambda_param / n_samples) * weights + gradient = (1 / n_samples) * (X.T @ (y_pred - y)) + (lambda_param / n_samples) * (weights[1:] ** 2) gradient[0] -= (lambda_param / n_samples) * weights[0] # Don't regularize the bias term return gradient @@ -30,8 +30,8 @@ def training(self, X, y, type, lr = 0.1, n_iters = 100): self.weights = np.zeros(n_features) for _ in range(n_iters): self.weights_history.append(self.weights[1]) - self.costs_history.append(self.costFunction(X, y)) y_pred = self.prediction(X) + self.costs_history.append(self.costFunction(X, y, y_pred)) weights_gradient = gradientDescent(X, y, y_pred, n_samples, self.lambda_param, self.weights) self.weights -= lr * weights_gradient else: @@ -52,8 +52,7 @@ def get_Costs_History(self): def prediction(self, X): return np.dot(X, self.weights) - def costFunction(self, X, y): - y_pred = self.prediction(X) + def costFunction(self, X, y, y_pred): mse = np.sum((y - y_pred) ** 2) regularization = self.lambda_param * np.sum(self.weights[1:] ** 2) return mse + regularization \ No newline at end of file From 460197e965231f4bf8c8539a4a739130f74d5387 Mon Sep 17 00:00:00 2001 From: Chonakan Chumtap Date: Sat, 3 Aug 2024 17:54:47 +0700 Subject: [PATCH 16/32] :memo: : added comment to 5.3. --- Regularization/scratching/5.3_vectorized.py | 26 +++++++++++++++++---- 1 file changed, 21 insertions(+), 5 deletions(-) diff --git a/Regularization/scratching/5.3_vectorized.py b/Regularization/scratching/5.3_vectorized.py index 65e37a2..24680ac 100644 --- a/Regularization/scratching/5.3_vectorized.py +++ b/Regularization/scratching/5.3_vectorized.py @@ -12,32 +12,40 @@ def define_X_include_bias(X): def problem(X): return np.sin(np.dot(np.pi, X)) +# Define a mean model calculation def meanModel(models): return np.mean(models, axis=0) +# Define a bias calculation def computeBias(mean_model): z = np.square(mean_model - y) return np.mean(z) +# Define a variance calculation def computeVariance(E_d, mean_model): z = np.square(E_d - mean_model) var_x = np.mean(z) return np.mean(var_x) +# Define a E out calculation def computeEout(bias, variance): return bias + variance if __name__ == "__main__": - # Generate X features + + # Generate X from -1 to 1 with a hundred values X = np.linspace(-1, 1, 100) - # Compute y values + + # Make y values from problem function (sin) y = problem(X) - # Define X data (bias + weigths) + # Add bias term to X at the first column X_b = define_X_include_bias(X) + # For collecting prediction value of each genaral data for both models E_d_linear = [] E_d_ridge = [] + # Initialize subplots fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) @@ -56,26 +64,34 @@ def computeEout(bias, variance): # Perform the training and plotting in the loop for _ in range(1000): + # Create Linear model linearModel = LinearRegression() + # Create Ridge model ridgeModel = RidgeRegression(lambda_param=0.5) # Randomly select 2 values from the dataset rands_X = np.random.choice(X, 2) - y_sample = problem(rands_X) + + # Create X and y from genaral value for training X_sample = define_X_include_bias(rands_X) + y_sample = problem(rands_X) + # Train model to fit the data with normal equation linearModel.training(X=X_sample, y=y_sample, type="normalEq") ridgeModel.training(X=X_sample, y=y_sample, type="normalEq") + # Make a prediction with the model that train only 2 data but use with all data for both models y_pred_lin = linearModel.prediction(X_b) E_d_linear.append(y_pred_lin) y_pred_ridge = ridgeModel.prediction(X_b) E_d_ridge.append(y_pred_ridge) + # Plot the prediction line for each iters with random genaral data ax1.plot(X, y_pred_lin, c="black", alpha=0.05) ax2.plot(X, y_pred_ridge, c="black", alpha=0.05) + # Calculate mean model, bias, varaince, and E out for both models to see the difference between non-regularization and regularization mean_linearModel = meanModel(E_d_linear) mean_ridgeModel = meanModel(E_d_ridge) @@ -100,9 +116,9 @@ def computeEout(bias, variance): ax1.set_title(f'Linear Regression \n Bias = {bias_linearModel:.2f} | Varinace = {variance_linearModel:.2f} | E out = {eOut_linearModel:.2f}') ax2.set_title(f'Ridge Regression \n Bias = {bias_ridgeModel:.2f} | Varinace = {variance_ridgeModel:.2f} | E out = {eOut_ridgeModel:.2f}') + # Plot the mean predictions line ax1.plot(X, mean_linearModel, c="red", label="Mean Model", linewidth=2) ax2.plot(X, mean_ridgeModel, c="red", label="Mean Model", linewidth=2) - # Display the plots plt.tight_layout() plt.show() \ No newline at end of file From c93a6a898910c340652a68f479f671b18fa82f00 Mon Sep 17 00:00:00 2001 From: Chonakan Chumtap Date: Sat, 3 Aug 2024 17:57:08 +0700 Subject: [PATCH 17/32] :memo: : added path to main README. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2c47d86..ca1c3b0 100644 --- a/README.md +++ b/README.md @@ -13,4 +13,4 @@ This is for ML class as a senior year, 2567. - [Linear Model](https://github.com/Kariusdi/Machine-Learning-Class/tree/main/Generalization/LinearModel) - [Linear Origin Model](https://github.com/Kariusdi/Machine-Learning-Class/tree/main/Generalization/LinearOriginModel) - [Learning Curve](https://github.com/Kariusdi/Machine-Learning-Class67/tree/main/LearningCurve) - +- [Regularization](https://github.com/Kariusdi/Machine-Learning-Class67/tree/main/Regularization) From 1830963f303786b3a4f8aa0b7222fcc1d39edc95 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Sat, 3 Aug 2024 18:08:44 +0700 Subject: [PATCH 18/32] Fix E_Out --- Regularization/scratching/Ridge_ass2.py | 72 ++++++++++++++++--------- 1 file changed, 46 insertions(+), 26 deletions(-) diff --git a/Regularization/scratching/Ridge_ass2.py b/Regularization/scratching/Ridge_ass2.py index f6c8979..22dc24e 100644 --- a/Regularization/scratching/Ridge_ass2.py +++ b/Regularization/scratching/Ridge_ass2.py @@ -1,35 +1,55 @@ import numpy as np import pandas as pd import matplotlib.pyplot as plt -from sklearn.model_selection import train_test_split from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error +from sklearn.model_selection import KFold def polyRidge(degree): # Create polynomial features poly = PolynomialFeatures(degree) # Adjust degree as needed X_poly = poly.fit_transform(X) - # Split data into training and testing sets - X_train, X_test, y_train, y_test = train_test_split(X_poly, y, test_size=0.2, random_state=42) - # Create and train the Ridge regression model - ridge = Ridge(alpha=100) # Adjust alpha for regularization strength - ridge.fit(X_train, y_train) - - # Make predictions on the test and training sets - y_pred_train = ridge.predict(X_train) - y_pred_test = ridge.predict(X_test) - - # Evaluate the model - # Calculate E-train and E-test (RMSE) - mse_train = mean_squared_error(y_train, y_pred_train) - mse_test = mean_squared_error(y_test, y_pred_test) - rmse_train = np.sqrt(mse_train) - rmse_test = np.sqrt(mse_test) + + # Split data for KFold cross-validation + kf = KFold(n_splits=10, shuffle=True, random_state=42) # Adjust n_splits + + # Initialize variables + mse_train_list = [] + mse_test_list = [] + + # Perform KFold cross-validation + for train_index, test_index in kf.split(X_poly): + X_train, X_test = X_poly[train_index], X_poly[test_index] + y_train, y_test = y[train_index], y[test_index] + + # Create and train the Ridge regression model + ridge = Ridge(alpha=10000) # Adjust alpha for regularization strength + ridge.fit(X_train, y_train) + + # Make predictions on the test and training sets + y_pred_train = ridge.predict(X_train) + y_pred_test = ridge.predict(X_test) + + # Evaluate the model + mse_train = mean_squared_error(y_train, y_pred_train) + mse_test = mean_squared_error(y_test, y_pred_test) + + mse_train_list.append(mse_train) + mse_test_list.append(mse_test) + + # Estimate bias (average training error) + E_train = np.sqrt(np.mean(mse_train_list)) + + # Estimate variance (average difference between training and test error) + E_var = np.sqrt(np.mean(np.square(np.array(mse_test_list) - np.mean(mse_train_list)))) + + # Estimated E_out (sum of bias and variance) + E_out = E_train + E_var # Print results and return errors - print(f"Degree={degree}: E_train: {rmse_train:.4f}, E_test: {rmse_test:.4f}") - return rmse_train, rmse_test + return E_train, E_out + def generate_sin(): np.random.seed(42) @@ -49,19 +69,19 @@ def import_csv(path): degrees = np.arange(1, 11) # Adjust the range as needed # Initialize lists to store errors -E_train_list = [] -E_test_list = [] +E_out_list = [] +E_in_List = [] # Call the function for each degree and store errors for deg in degrees: - rmse_train, rmse_test = polyRidge(deg) - E_train_list.append(rmse_train) - E_test_list.append(rmse_test) + E_out, E_in = polyRidge(deg) + E_out_list.append(E_out) + E_in_List.append(E_in) # Plot the results plt.figure(figsize=(8, 6)) -plt.plot(degrees, E_train_list, label="E_train", marker='o', linestyle='-') -plt.plot(degrees, E_test_list, label="E_test", marker='s', linestyle='-') +plt.plot(degrees, E_out_list, label="E_Out", marker='o', linestyle='-') +plt.plot(degrees, E_in_List, label="E_In", marker='s', linestyle='-') plt.xlabel("Model Complexity (Degree)") plt.ylabel("RMSE") plt.title("Model Complexity vs. E_train and E_test") From a33bd7463a97c997f701097a7d06ba71a90ca51e Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Sat, 3 Aug 2024 21:26:11 +0700 Subject: [PATCH 19/32] Add new version ridge RMSE --- Regularization/scratching/Ridge_ass2.py | 3 +- Regularization/scratching/lamda.py | 59 +++++++++++++++++++++++++ 2 files changed, 60 insertions(+), 2 deletions(-) create mode 100644 Regularization/scratching/lamda.py diff --git a/Regularization/scratching/Ridge_ass2.py b/Regularization/scratching/Ridge_ass2.py index 22dc24e..aead7fc 100644 --- a/Regularization/scratching/Ridge_ass2.py +++ b/Regularization/scratching/Ridge_ass2.py @@ -24,7 +24,7 @@ def polyRidge(degree): y_train, y_test = y[train_index], y[test_index] # Create and train the Ridge regression model - ridge = Ridge(alpha=10000) # Adjust alpha for regularization strength + ridge = Ridge(alpha=100000) # Adjust alpha for regularization strength ridge.fit(X_train, y_train) # Make predictions on the test and training sets @@ -50,7 +50,6 @@ def polyRidge(degree): # Print results and return errors return E_train, E_out - def generate_sin(): np.random.seed(42) X = np.random.rand(100, 1) * 10 diff --git a/Regularization/scratching/lamda.py b/Regularization/scratching/lamda.py new file mode 100644 index 0000000..e7a41ae --- /dev/null +++ b/Regularization/scratching/lamda.py @@ -0,0 +1,59 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.linear_model import Ridge +from sklearn.metrics import mean_squared_error + +def generate_sin(): + np.random.seed(42) + X = np.random.rand(100, 1) * 10 + Y = np.sin(X) + np.random.randn(100) / 10 + X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0) + return X_train, Y_train, X_test, Y_test + +def import_csv(path): + df = pd.read_csv(path) + X = df['Height'].values.reshape(-1, 1) # Reshape for sklearn + Y = df['Weight'].values + X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0) + return X_train, Y_train, X_test, Y_test + +def ridge_regression(X_train, Y_train, X_test, Y_test, alpha): + model = Ridge(alpha=alpha) + model.fit(X_train, Y_train) + + train_rmse = mean_squared_error(Y_train, model.predict(X_train)) + test_rmse = mean_squared_error( Y_test, model.predict(X_test)) + + return train_rmse, test_rmse + +def plot_rmse_vs_alpha(alphas, train_rmse, test_rmse): + + plt.figure(figsize=(8, 6)) + plt.plot(alphas, train_rmse, label="Train", marker='o', linestyle='-') + plt.plot(alphas, test_rmse, label="Test", marker='s', linestyle='-') + plt.xlabel("Model Complexity (Log scale)") + plt.xscale('log') + plt.ylabel("RMSE") + plt.title("Model Complexity vs. E_train and E_test") + plt.legend() + plt.grid(True) + plt.tight_layout() + plt.show() + +X_train, Y_train, X_test, Y_test = import_csv("Regularization/dataset/HeightWeight.csv") +#X_train, Y_train, X_test, Y_test = generate_sin() + +alphas = np.arange(1, 100000, 100) +E_train = [] +E_test = [] + +for alpha_ in alphas: + train_rmse, test_rmse = ridge_regression(X_train, Y_train, X_test, Y_test, alpha_) + E_train.append(np.sqrt(train_rmse)) + E_test.append(np.sqrt(test_rmse)) + +plot_rmse_vs_alpha(alphas, E_train, E_test) + + From 0828d3c9a1a2ce3c6d1499c9ea60622781d09b28 Mon Sep 17 00:00:00 2001 From: "[Sirirwat]" Date: Sat, 3 Aug 2024 21:54:42 +0700 Subject: [PATCH 20/32] Delete --- LearningCurve.zip | Bin 0 -> 456114 bytes .../Regularization/ridge.ipynb | 255 ------------------ 2 files changed, 255 deletions(-) create mode 100644 LearningCurve.zip delete mode 100644 Performance-Estimation/Regularization/ridge.ipynb diff --git a/LearningCurve.zip b/LearningCurve.zip new file mode 100644 index 0000000000000000000000000000000000000000..acc3499f183463f884ccb09deadae6cd567eaef2 GIT binary patch literal 456114 zcmZ^JLy#y6kYwAoZQJgCZQHhO+qP}nwr$%s=kLT^HzNv{JZB;0rGP`d^hcF>tiCur(8Qadb7IH*j(?adx7Y|33=E|57X!c{fGn005R%000R8e-tA- zTPJ4&TW30ZTeE8~6J;H#)tiI;TO^3v+grMUogK*kauApsQ1Ct)OFOx{eV~`Un^)FW z+t=0YL(kvcm{GJ(tLn;TmFf(u*$t7Th5{BQAU+@n5aHCMczK{eynDX`GY-tX6%J__ zG!S9LdUzD|RAf{VMHh$WW_KGvfN`N>vwq8p@8R*Kk0inX0VI@U?l=))5sakh(vR>u 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