def tune_rf(self, X, y):
msg.print_line()
msg.tune_rf_message()
estimators = None
features = None
leaf = None
msg.loading_message()
rf_params = self.mysql_cn.read('select * from params_rf;')
n_estimators = rf_params['n_estimators'].tolist()
max_features = rf_params['max_features'].tolist()
min_samples_leaf = rf_params['min_samples_leaf'].tolist()
if not n_estimators or not max_features or not min_samples_leaf:
msg.tuning_message()
param_grid = {
'n_estimators': [10],
'max_features': ['auto', 'sqrt', 'log2'],
'min_samples_leaf': [1, 5, 10]
}
CV_rf = GridSearchCV(estimator=RF(), param_grid=param_grid, cv=5)
CV_rf.fit(X, y)
rf_param = CV_rf.best_params_
n_estimators = rf_param['n_estimators']
max_features = rf_param['max_features']
min_samples_leaf = rf_param['min_samples_leaf']
msg.print_rf_params(n_estimators, max_features, min_samples_leaf)
msg.insert_message()
self.mysql_cn.insert_update("INSERT INTO params_rf(n_estimators, max_features, min_samples_leaf) "
"VALUES(%d, '%s', %d)" % (n_estimators, max_features, min_samples_leaf))
return (n_estimators, max_features, min_samples_leaf)
elif self.check_tune[0]:
msg.tuning_message()
param_grid = {
'n_estimators': [10, 100],
'max_features': ['auto', 'sqrt', 'log2'],
'min_samples_leaf': [1, 5, 10]
}
CV_rf = GridSearchCV(estimator=RF(), param_grid=param_grid, cv=5)
CV_rf.fit(X, y)
rf_param = CV_rf.best_params_
n_estimators = rf_param['n_estimators']
max_features = rf_param['max_features']
min_samples_leaf = rf_param['min_samples_leaf']
msg.print_rf_params(n_estimators, max_features, min_samples_leaf)
msg.update_message()
self.mysql_cn.insert_update(
"UPDATE params_rf SET n_estimators = %d, max_features = '%s', min_samples_leaf = %d"
% (n_estimators, max_features, min_samples_leaf))
return (n_estimators, max_features, min_samples_leaf)
else:
msg.loading_message()
new_rf_params = self.mysql_cn.read('select * from params_rf;')
estimators = new_rf_params['n_estimators'].tolist()
features = new_rf_params['max_features'].tolist()
leaf = new_rf_params['min_samples_leaf'].tolist()
n_estimators = estimators[0]
max_features = features[0]
min_samples_leaf = leaf[0]
msg.print_rf_params(n_estimators, max_features, min_samples_leaf)
return (n_estimators, max_features, min_samples_leaf)
def tune_svm(self, X, y):
msg.print_line()
msg.tune_svm_message()
C_range = np.logspace(-2, 2, 9)
gamma_range = np.logspace(-2, 2, 9)
param_grid = [{'kernel': ['rbf'], 'gamma': gamma_range, 'C': C_range}]
msg.loading_message()
svm_params = self.mysql_cn.read('select * from params_svm;')
kernel = svm_params['kernel'].tolist()
c = svm_params['c'].tolist()
gamma = svm_params['gamma'].tolist()
if not kernel:
msg.tuning_message()
CV_svm = GridSearchCV(SVC(), param_grid=param_grid, cv=5)
CV_svm.fit(X, y)
svm_params = CV_svm.best_params_
kernel = svm_params['kernel']
c = svm_params['C']
gamma = svm_params['gamma']
msg.print_svm_params(kernel, c, gamma)
msg.insert_message()
self.mysql_cn.insert_update("INSERT INTO params_svm(kernel, c, gamma) "
"VALUES('%s', %s, %s)" % (kernel, c, gamma))
return (kernel, c, gamma)
elif self.check_tune[0]:
msg.tuning_message()
CV_svm = GridSearchCV(SVC(), param_grid=param_grid, cv=5)
CV_svm.fit(X, y)
svm_params = CV_svm.best_params_
kernel = svm_params['kernel']
c = svm_params['C']
gamma = svm_params['gamma']
msg.print_svm_params(kernel, c, gamma)
msg.update_message()
self.mysql_cn.insert_update(
"UPDATE params_svm SET kernel = '%s', c = %s, gamma = %s"
% (kernel, c, gamma))
return (kernel, c, gamma)
else:
msg.loading_message()
new_svm_params = self.mysql_cn.read('select * from params_svm;')
kernel = new_svm_params['kernel'].tolist()
c = new_svm_params['c'].tolist()
gamma = new_svm_params['gamma'].tolist()
msg.print_svm_params(kernel[0], c[0], gamma[0])
return (kernel[0], c[0], gamma[0])
def tune_knn(self, X, y):
msg.tune_knn_message()
k_value = None
msg.loading_message()
k_params = self.mysql_cn.read('select * from params_knn;')
k = k_params['k_value'].tolist()
if not k:
msg.tuning_message()
range_k = list(range(1, 31))
param_grid = {
'n_neighbors': range_k
}
CV_knn = GridSearchCV(estimator=KNN(), param_grid=param_grid, cv=10)
CV_knn.fit(X, y)
k_value_param = CV_knn.best_params_
k_value = k_value_param['n_neighbors']
k = k_value
msg.print_knn_params(k)
msg.insert_message()
self.mysql_cn.insert_update("INSERT INTO params_knn(k_value) VALUES(%d)" % k_value)
return k
elif self.check_tune[0]:
msg.tuning_message()
range_k = list(range(1, 31))
param_grid = {
'n_neighbors': range_k
}
CV_knn = GridSearchCV(estimator=KNN(), param_grid=param_grid, cv=10)
CV_knn.fit(X, y)
k_value_param = CV_knn.best_params_
k_value = k_value_param['n_neighbors']
k = k_value
msg.print_knn_params(k)
msg.update_message()
self.mysql_cn.insert_update("UPDATE params_knn SET k_value=%d" % k_value)
return k
else:
msg.loading_message()
new_k = self.mysql_cn.read('select * from params_knn;')
k_value = new_k['k_value'].tolist()
k = k_value[0]
msg.print_knn_params(k)
return k
import pandas
import numpy as np
from sklearn.neighbors import KNeighborsClassifier as KNN
import Tune_Params as tune
import DBAccess as db_connect
import Messages as msg
from sklearn.externals import joblib
mysql_cn = db_connect.DBConnect()
mysql_cn.insert_update("UPDATE tune_user_selection SET is_tune_needed = 0")
tune_params = tune.TuneParams()
# Read in the data.
msg.loading_message()
df_mysql = mysql_cn.read('select * from employeesit_raw_train')
col_names = df_mysql.columns.tolist()
# Isolate target data
churn_result = df_mysql['churn']
y = churn_result
# Don't need these columns
to_drop = ['Employee_ID', 'Employee_Name', 'Reason_To_Leave', 'churn']
churn_columns = df_mysql.drop(to_drop, axis=1)
# Pull out features for future use
features = churn_columns.columns
X = churn_columns.as_matrix().astype(np.float)
# Standardizing the features so that they are
