# 11 is treatments (1,2,3,4,5,6) categories # This will train for the first regressive value model = baseRegressor(raw_data=df,feature_stop=265,target_column=267, regressor=True, existing_weights=[-0.10,0.10], features_to_use=wt,stoppage=80000); model.fit(alpha=0.0000000000001,target_error=0.00001); # Best I could find for 268 """ # This will train for the last regressive value model = baseRegressor(raw_data=df,feature_stop=265,target_column=268, regressor=True,features_to_use=wt,stoppage=10000000); model.fit(alpha=0.000000000001,target_error=1); # Best I could find for 268 """ print model.weights; # Returns a list of random values. predictions = model.predict(validation); answers = return_single_column(validation,dex=267); print "Mean-Squared Error: " + str(mse(answers,predictions)); """ l = map(abs, model.weights); b = sorted(range(len(l)),key=lambda k: l[k]); b.reverse() print b; # Print out the most important features by weight ys = return_single_column(df,dex=DAT); print ys[0:10]; print model.predict(df[0:10]); """
# Taking the first 20 variance problems #wt = [12, 21, 267, 25, 266, 20, 11, 28, 27, 16, 26, 32, 13, 29, 2, 33, 1, 17, 30, 14] wt = [12, 21, 25, 20, 11, 28, 27, 16, 26, 32, 13, 29, 2, 33, 1, 17, 30, 14] # categorical: [ 1, 4, 5, 6, 7, 8, 9, 10, 11 ] x # 11 is treatments (1,2,3,4,5,6) categories # I changed alpha by 1 decimal place # Below will be the classification problem model = baseRegressor(raw_data=training,feature_stop=265,target_column=266, regressor=False,features_to_use=wt,stoppage=5000000); model.fit(alpha=0.0000000001,target_error=0.1); # For Classification print model.weights; # Need to test that I can insert weights correctly here predictions = model.predict(validation); answers = return_single_column(validation,dex=266); print "Percentage Correct: " + str(binary_cv(predictions,answers)); """ # This will train for the first regressive value model = baseRegressor(raw_data=df,feature_stop=265,target_column=267, regressor=True,features_to_use=wt,stoppage=10000000); model.fit(alpha=0.000000000001,target_error=1); # Best I could find for 268 # This will train for the last regressive value model = baseRegressor(raw_data=df,feature_stop=265,target_column=268, regressor=True,features_to_use=wt,stoppage=10000000); model.fit(alpha=0.000000000001,target_error=1); # Best I could find for 268 """ print model.weights; # Returns a list of random values.