89 lines
No EOL
2.6 KiB
Python
89 lines
No EOL
2.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sat Mar 5 12:57:32 2022
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@author: tanu
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"""
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#%%
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# data, etc for now comes from my_data6.py and/or my_data5.py
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#%%
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homedir = os.path.expanduser("~")
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os.chdir(homedir + "/git/ML_AI_training/")
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# my function
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from MultClassPipe2 import MultClassPipeline2
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#%% try combinations
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#import sys, os
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#os.system("imports.py")
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def precision(y_true,y_pred):
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return precision_score(y_true,y_pred,pos_label = 1)
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def recall(y_true,y_pred):
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return recall_score(y_true, y_pred, pos_label = 1)
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def f1(y_true,y_pred):
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return f1_score(y_true, y_pred, pos_label = 1)
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#%%
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numerical_features_df.shape
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categorical_features_df.shape
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all_features_df.shape
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all_features_df.dtypes
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#%%
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target = target1
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#target = target3
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X_trainN, X_testN, y_trainN, y_testN = train_test_split(numerical_features_df,
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target,
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test_size = 0.33,
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random_state = 42)
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X_trainC, X_testC, y_trainC, y_testC = train_test_split(categorical_features_df,
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target,
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test_size = 0.33,
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random_state = 42)
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X_train, X_test, y_train, y_test = train_test_split(all_features_df,
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target,
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test_size = 0.33,
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random_state = 42)
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#%%
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#%% with feature selection
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# Determine categorical and numerical features
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input_df = numerical_features_df.copy()
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#input_df = categorical_features_df
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#input_df = all_features_df
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numerical_ix = all_features_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = all_features_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# prepare data
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix)]
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col_transform = ColumnTransformer(transformers = t
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, remainder = 'passthrough')
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# model pipeline
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model = Pipeline(steps=[('prep', col_transform)
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, ('classifier', LogisticRegression())])
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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y_pred
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selector_log = RFECV(estimator = model
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, cv = 10
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, step = 1)
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selector_log_x = selector_log.fit_transform(X_train, y_train)
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print(selector_log_x.get_support())
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X_trainN.columns
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print(selector_logistic_x.ranking_) |