From 4cc401a5dda6b970b8ed0f153c4ca9d759ff2b49 Mon Sep 17 00:00:00 2001
From: msonumanish <msonu.manish@gmail.com>
Date: Mon, 6 Nov 2017 02:24:28 +0000
Subject: [PATCH] Done

---
 __init__.pyc                             | Bin 180 -> 170 bytes
 q01_pipeline/__init__.pyc                | Bin 193 -> 183 bytes
 q01_pipeline/build.py                    |  42 +++++++++++++++++++++++
 q01_pipeline/build.pyc                   | Bin 2322 -> 1885 bytes
 q01_pipeline/tests/__init__.pyc          | Bin 199 -> 189 bytes
 q01_pipeline/tests/test_q01_pipeline.pyc | Bin 2221 -> 2089 bytes
 6 files changed, 42 insertions(+)

diff --git a/__init__.pyc b/__init__.pyc
index d1c94d3c52cde5a38567928948231f3d1adc4dec..a637ea8cc96d9a009fbf9209cc03346b92748bd8 100644
GIT binary patch
delta 61
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delta 71
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diff --git a/q01_pipeline/__init__.pyc b/q01_pipeline/__init__.pyc
index b360a57252ba2db1b27d2b2e17e906e3f57cb43b..e06fba0cef4baeb3019e77544e14d60c404c95e7 100644
GIT binary patch
delta 74
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delta 84
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diff --git a/q01_pipeline/build.py b/q01_pipeline/build.py
index 96beca7..f0e31fa 100644
--- a/q01_pipeline/build.py
+++ b/q01_pipeline/build.py
@@ -1,3 +1,4 @@
+# %load q01_pipeline/build.py
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split, GridSearchCV
@@ -5,9 +6,50 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.utils.class_weight import compute_class_weight
 from sklearn.metrics import roc_auc_score
+from sklearn.metrics import precision_score, recall_score
+from sklearn.metrics import f1_score, confusion_matrix
+#from imblearn.over_sampling import SMOTE
 
 bank = pd.read_csv('data/Bank_data_to_class.csv', sep=',')
+label_enc = LabelEncoder()
+for column in bank.select_dtypes(include=["object"]).columns.values:
+    bank[column] = label_enc.fit_transform(bank[column])
 
+#bank.replace({0:1, 1:0}, inplace=True)
 # Write your solution here :
+y=bank['y']
+X=bank.iloc[:,:-1]
+#print(y)
 
+X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3, random_state=9)
 
+model = RandomForestClassifier(random_state=9,class_weight='balanced')
+
+
+def pipeline(X_train,X_test,y_train,y_test,model):
+
+    param_grid = {
+              "n_estimators": [10, 50, 120],
+              "max_depth": [40, 20, 10],
+              "max_leaf_nodes": [5, 10, 2]}
+
+    grid_obj = GridSearchCV(model, param_grid,cv=5)
+    grid_obj.fit(X_train,y_train)
+    y_pred=grid_obj.predict(X_test)
+    acc_score= roc_auc_score(y_test,y_pred)
+    return grid_obj, acc_score.item()
+
+
+
+
+# grid_model,  auc_score= pipeline(X_train,X_test,y_train,y_test,model)
+# print(auc_score)
+# bank_test = pd.read_csv('data/Bank_data_to_test.csv')
+# y = bank_test['y']
+# X = bank_test.drop(['y'], axis=1)
+# prediction = grid_model.predict_proba(X)[:, 1]
+# #y = bank_test['y']
+# print(type(auc_score))
+
+# auc_score_test = roc_auc_score(y, prediction)
+# print(auc_score_test)
diff --git a/q01_pipeline/build.pyc b/q01_pipeline/build.pyc
index 5a9b3ad55dc3cef2130ad4675212f4c1bab540ed..76b70a03cff6ea957fc6813ac4dee49d2dc3d2df 100644
GIT binary patch
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diff --git a/q01_pipeline/tests/__init__.pyc b/q01_pipeline/tests/__init__.pyc
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GIT binary patch
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diff --git a/q01_pipeline/tests/test_q01_pipeline.pyc b/q01_pipeline/tests/test_q01_pipeline.pyc
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