def main():
#run the two datasets
data_sets = ["basic_data", "basic_data_only_finishers", "basic_data_clean_lecture", "basic_data_piazza", "basic_data_piazza_only_finishers"]
for data_set in data_sets:
#load the data
headings, data = data_work.load_data(data_set + ".csv", conversion_function = data_work.convert_survey_data, max_records = None)
visualize_correlelations(data, headings, data_set)
#draw scatterplots in matrix form
scatterplot_matrix(data, headings, data_set)
#generate the stats
generate_stats(data, headings, data_set)
#draw charts for the relationships between variables
draw_charts(data, headings, data_set)
#load unaltered data
headings, data = data_work.load_data(data_set + ".csv", conversion_function = None, max_records = None)
#draw some frequency histograms
draw_histograms(data, headings, data_set)
return
def main():
#run the two datasets
data_sets = [
"basic_data", "basic_data_only_finishers", "basic_data_clean_lecture",
"basic_data_piazza", "basic_data_piazza_only_finishers"
]
for data_set in data_sets:
#load the data
headings, data = data_work.load_data(
data_set + ".csv",
conversion_function=data_work.convert_survey_data,
max_records=None)
#generate the stats
generate_stats(data, headings, data_set)
#draw charts for the relationships between variables
draw_charts(data, headings, data_set)
#load unaltered data
headings, data = data_work.load_data(data_set + ".csv",
conversion_function=None,
max_records=None)
#draw some frequency histograms
draw_histograms(data, headings, data_set)
return
def main():
#create a dictionary of feature setups
#select the appropriate columns
feature_dict = {
#"Full" : header.tolist(),
"Lecture Views" : ['overal_lecture_views', 'total_lecture_time'],
"Piazza Use" : ['mid_on_piazza', 'final_on_piazza', 'piazza_posts', 'piazza_days', 'piazza_views'],
"Lecture Pace" : ['lecture_1_pace_Late', 'lecture_1_pace_On-time', 'lecture_1_pace_Unknown', 'lecture_2_pace_Late', 'lecture_2_pace_On-time', 'lecture_2_pace_Unknown', 'lecture_3_pace_Late', 'lecture_3_pace_On-time', 'lecture_3_pace_Unknown', 'lecture_4_pace_Early', 'lecture_4_pace_Late', 'lecture_4_pace_On-time', 'lecture_4_pace_Unknown', 'lecture_5_pace_Early', 'lecture_5_pace_Late', 'lecture_5_pace_On-time', 'lecture_5_pace_Unknown', 'lecture_6_pace_Early', 'lecture_6_pace_Late', 'lecture_6_pace_On-time', 'lecture_6_pace_Unknown', 'lecture_7_pace_Early', 'lecture_7_pace_Late', 'lecture_7_pace_On-time', 'lecture_7_pace_Unknown', 'lecture_8_pace_Early', 'lecture_8_pace_Late', 'lecture_8_pace_On-time', 'lecture_8_pace_Unknown', 'lecture_9_pace_Early', 'lecture_9_pace_Late', 'lecture_9_pace_On-time', 'lecture_9_pace_Unknown', 'lecture_10_pace_Early', 'lecture_10_pace_Late', 'lecture_10_pace_On-time', 'lecture_10_pace_Unknown', 'lecture_11_pace_Early', 'lecture_11_pace_Late', 'lecture_11_pace_On-time', 'lecture_11_pace_Unknown', 'lecture_12_pace_Early', 'lecture_12_pace_Late', 'lecture_12_pace_On-time', 'lecture_12_pace_Unknown', 'lecture_13_pace_Early', 'lecture_13_pace_Late', 'lecture_13_pace_On-time', 'lecture_13_pace_Unknown', 'lecture_14_pace_Early', 'lecture_14_pace_Late', 'lecture_14_pace_On-time', 'lecture_14_pace_Unknown', 'lecture_15_pace_Early', 'lecture_15_pace_Late', 'lecture_15_pace_On-time', 'lecture_15_pace_Unknown', 'lecture_16_pace_Early', 'lecture_16_pace_Late', 'lecture_16_pace_On-time', 'lecture_16_pace_Unknown', 'lecture_17_pace_Early', 'lecture_17_pace_Late', 'lecture_17_pace_On-time', 'lecture_17_pace_Unknown', 'lecture_18_pace_Early', 'lecture_18_pace_Late', 'lecture_18_pace_On-time', 'lecture_18_pace_Unknown', 'lecture_19_pace_Early', 'lecture_19_pace_Late', 'lecture_19_pace_On-time', 'lecture_19_pace_Unknown', 'lecture_20_pace_Early', 'lecture_20_pace_Late', 'lecture_20_pace_On-time', 'lecture_20_pace_Unknown', 'lecture_21_pace_Early', 'lecture_21_pace_Late', 'lecture_21_pace_On-time', 'lecture_21_pace_Unknown', 'lecture_22_pace_Early', 'lecture_22_pace_Late', 'lecture_22_pace_On-time', 'lecture_22_pace_Unknown', 'lecture_23_pace_Early', 'lecture_23_pace_Late', 'lecture_23_pace_On-time', 'lecture_23_pace_Unknown', 'lecture_24_pace_Early', 'lecture_24_pace_Late', 'lecture_24_pace_On-time', 'lecture_24_pace_Unknown', 'lecture_25_pace_Early', 'lecture_25_pace_Late', 'lecture_25_pace_On-time', 'lecture_25_pace_Unknown', 'lecture_26_pace_Early', 'lecture_26_pace_Late', 'lecture_26_pace_On-time', 'lecture_26_pace_Unknown', 'overall_pace_Early', 'overall_pace_Late', 'overall_pace_On-time', 'overall_pace_Unknown'],
"Classmate Contact" : ['qtr_on_piazza', 'qtr_email', 'qtr_hipchat', 'qrt_gplus', 'qtr_other_chat', 'qtr_phone', 'qtr_facebook', 'qtr_in_person', 'mid_on_piazza', 'mid_email', 'mid_hipchat', 'qrt_gplus', 'mid_other_chat', 'mid_phone', 'mid_facebook', 'mid_in_person', 'final_on_piazza', 'final_email', 'final_hipchat', 'qrt_gplus', 'final_other_chat', 'final_phone', 'final_facebook', 'final_in_person'],
"Lecture Amount" : ['total_lecture_time', 'overal_lecture_views', 'lecture_1_views', 'lecture_2_views', 'lecture_3_views', 'lecture_4_views', 'lecture_5_views', 'lecture_6_views', 'lecture_7_views', 'lecture_8_views', 'lecture_9_views', 'lecture_10_views', 'lecture_11_views', 'lecture_12_views', 'lecture_13_views', 'lecture_14_views', 'lecture_15_views', 'lecture_16_views', 'lecture_17_views', 'lecture_18_views', 'lecture_19_views', 'lecture_20_views', 'lecture_21_views', 'lecture_22_views', 'lecture_23_views', 'lecture_24_views', 'lecture_25_views', 'lecture_26_views'],
"Prior Experience" : ['formal_class_prog_taken', 'C', 'C#', 'C++', 'Java', 'JavaScript', 'Lisp', 'Objective C', 'Perl', 'PHP', 'Python', 'Ruby', 'Shell', 'Swift', 'Visual Basic', 'Other (specify below)', 'years_programming', 'prior_omscs_classes_completed', 'occupation', 'highest_education', 'besides_KBAI_how_many_classes', 'moocs_completed_outside_OMSCS'],
"Self Assesment" : ['qtr_proj1_confidence_neither confident nor unconfident', 'qtr_proj1_confidence_no answer', 'qtr_proj1_confidence_somewhat confident', 'qtr_proj1_confidence_somewhat unconfident', 'qtr_proj1_confidence_very confident', 'qtr_proj1_confidence_very unconfident', 'qtr_proj2_confidence_neither confident nor unconfident', 'qtr_proj2_confidence_no answer', 'qtr_proj2_confidence_somewhat confident', 'qtr_proj2_confidence_somewhat unconfident', 'qtr_proj2_confidence_very confident', 'qtr_proj2_confidence_very unconfident', 'mid_proj2_confidence_neither confident nor unconfident', 'mid_proj2_confidence_no answer', 'mid_proj2_confidence_somewhat confident', 'mid_proj2_confidence_somewhat unconfident', 'mid_proj2_confidence_very confident', 'mid_proj2_confidence_very unconfident', 'mid_proj3_confidence_neither confident nor unconfident', 'mid_proj3_confidence_no answer', 'mid_proj3_confidence_somewhat confident', 'mid_proj3_confidence_somewhat unconfident', 'mid_proj3_confidence_very confident', 'mid_proj3_confidence_very unconfident', 'final_proj3_confidence_neither confident nor unconfident', 'final_proj3_confidence_no answer', 'final_proj3_confidence_somewhat confident', 'final_proj3_confidence_somewhat unconfident', 'final_proj3_confidence_very confident', 'final_proj3_confidence_very unconfident']
}
#add the assignments in as variables to test too
expanded_feature_dict = feature_dict.copy()
for feature_set_name, select_columns in feature_dict.iteritems():
#create the new feature set names
feature_plus_a1 = feature_set_name + " After A1"
feature_plus_a2 = feature_set_name + " After A2"
feature_plus_a3 = feature_set_name + " After A3"
#add the new columns
select_columns_plus_a1 = select_columns + ["Assig_1_full_40", "proj_1_100"]
select_columns_plus_a2 = select_columns_plus_a1 + ["Assig_2_full_40", "proj_2_100"]
select_columns_plus_a3 = select_columns_plus_a2 + ["Assig_3_full_40", "proj_3_100"]
#add to the dictionary
expanded_feature_dict[feature_plus_a1] = select_columns_plus_a1
expanded_feature_dict[feature_plus_a2] = select_columns_plus_a2
expanded_feature_dict[feature_plus_a3] = select_columns_plus_a3
#replace the dictionary
feature_dict = expanded_feature_dict.copy()
#list the data sources
data_sources = [
"basic_data",
"basic_data_only_finishers",
"basic_data_clean_lecture",
"basic_data_piazza",
"basic_data_piazza_only_finishers"
]
#create csv for results
with open(os.path.join(results_location, 'regression_results.csv'), 'wb') as output_file:
#establish the csv writer
writer = csv.writer(output_file, delimiter=',')
for data_source in data_sources:
print("\n\n------------------")
print("Data Set - %s" % data_source)
print("------------------")
#this section determines R^2 scores of the regressors
writer.writerow(["R^2 Scores"])
writer.writerow(["Dataset - %s" % data_source] )
#load the data from the csv
header, data = data_work.load_data(data_source + ".csv", conversion_function = data_work.convert_survey_data, max_records = None)
#create headings
writer.writerow(["Feature", "Decision Tree", "Boosting", "Random Forest", "Linear Regression", "Support Vector Machine", "", "Feature Details"])
#loop through all the feature set combos
for feature_set_name, select_columns in feature_dict.iteritems():
print("\n\n------------------")
print("Feature Set - %s" % feature_set_name)
print("------------------")
#get the data subset
header_subset, data_subset = data_work.select_data_columns(header, data, column_names = ['course_grade'] + select_columns)
#first run on the full set
#assumes first column is Y
X_train, X_test, y_train, y_test = data_work.divide_for_training(data_subset)
#remove the label header
header_subset = header_subset[1 : ]
#scale the data
X_train, X_test = data_work.scale_features(X_train, X_test)
#test all to start
run_regressors(X_train, y_train, X_test, y_test, header_subset, writer, data_source + "-" + feature_set_name + " Linear")
#for degree in [2, 3, 4]:
for degree in [2]:
#convert to polynomials
poly = PolynomialFeatures(degree=degree)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.fit_transform(X_test)
#test all in poly
run_regressors(X_train_poly , y_train, X_test_poly, y_test, header_subset, writer, data_source + "-" + feature_set_name + " Poly %i" % degree)
##test individually
#for i in range(0, X_train.shape[1]):
# run_regressors(X_train[:, i,np.newaxis], y_train, X_test[:, i,np.newaxis], y_test, header_subset[i + 1, np.newaxis], writer)
return
def main():
#create a dictionary of feature setups
#select the appropriate columns
feature_dict = {
#"Full" : header.tolist(),
"Lecture Views": ['overal_lecture_views', 'total_lecture_time'],
"Piazza Use": [
'mid_on_piazza', 'final_on_piazza', 'piazza_posts', 'piazza_days',
'piazza_views'
],
"Lecture Pace": [
'lecture_1_pace_Late', 'lecture_1_pace_On-time',
'lecture_1_pace_Unknown', 'lecture_2_pace_Late',
'lecture_2_pace_On-time', 'lecture_2_pace_Unknown',
'lecture_3_pace_Late', 'lecture_3_pace_On-time',
'lecture_3_pace_Unknown', 'lecture_4_pace_Early',
'lecture_4_pace_Late', 'lecture_4_pace_On-time',
'lecture_4_pace_Unknown', 'lecture_5_pace_Early',
'lecture_5_pace_Late', 'lecture_5_pace_On-time',
'lecture_5_pace_Unknown', 'lecture_6_pace_Early',
'lecture_6_pace_Late', 'lecture_6_pace_On-time',
'lecture_6_pace_Unknown', 'lecture_7_pace_Early',
'lecture_7_pace_Late', 'lecture_7_pace_On-time',
'lecture_7_pace_Unknown', 'lecture_8_pace_Early',
'lecture_8_pace_Late', 'lecture_8_pace_On-time',
'lecture_8_pace_Unknown', 'lecture_9_pace_Early',
'lecture_9_pace_Late', 'lecture_9_pace_On-time',
'lecture_9_pace_Unknown', 'lecture_10_pace_Early',
'lecture_10_pace_Late', 'lecture_10_pace_On-time',
'lecture_10_pace_Unknown', 'lecture_11_pace_Early',
'lecture_11_pace_Late', 'lecture_11_pace_On-time',
'lecture_11_pace_Unknown', 'lecture_12_pace_Early',
'lecture_12_pace_Late', 'lecture_12_pace_On-time',
'lecture_12_pace_Unknown', 'lecture_13_pace_Early',
'lecture_13_pace_Late', 'lecture_13_pace_On-time',
'lecture_13_pace_Unknown', 'lecture_14_pace_Early',
'lecture_14_pace_Late', 'lecture_14_pace_On-time',
'lecture_14_pace_Unknown', 'lecture_15_pace_Early',
'lecture_15_pace_Late', 'lecture_15_pace_On-time',
'lecture_15_pace_Unknown', 'lecture_16_pace_Early',
'lecture_16_pace_Late', 'lecture_16_pace_On-time',
'lecture_16_pace_Unknown', 'lecture_17_pace_Early',
'lecture_17_pace_Late', 'lecture_17_pace_On-time',
'lecture_17_pace_Unknown', 'lecture_18_pace_Early',
'lecture_18_pace_Late', 'lecture_18_pace_On-time',
'lecture_18_pace_Unknown', 'lecture_19_pace_Early',
'lecture_19_pace_Late', 'lecture_19_pace_On-time',
'lecture_19_pace_Unknown', 'lecture_20_pace_Early',
'lecture_20_pace_Late', 'lecture_20_pace_On-time',
'lecture_20_pace_Unknown', 'lecture_21_pace_Early',
'lecture_21_pace_Late', 'lecture_21_pace_On-time',
'lecture_21_pace_Unknown', 'lecture_22_pace_Early',
'lecture_22_pace_Late', 'lecture_22_pace_On-time',
'lecture_22_pace_Unknown', 'lecture_23_pace_Early',
'lecture_23_pace_Late', 'lecture_23_pace_On-time',
'lecture_23_pace_Unknown', 'lecture_24_pace_Early',
'lecture_24_pace_Late', 'lecture_24_pace_On-time',
'lecture_24_pace_Unknown', 'lecture_25_pace_Early',
'lecture_25_pace_Late', 'lecture_25_pace_On-time',
'lecture_25_pace_Unknown', 'lecture_26_pace_Early',
'lecture_26_pace_Late', 'lecture_26_pace_On-time',
'lecture_26_pace_Unknown', 'overall_pace_Early',
'overall_pace_Late', 'overall_pace_On-time', 'overall_pace_Unknown'
],
"Classmate Contact": [
'qtr_on_piazza', 'qtr_email', 'qtr_hipchat', 'qrt_gplus',
'qtr_other_chat', 'qtr_phone', 'qtr_facebook', 'qtr_in_person',
'mid_on_piazza', 'mid_email', 'mid_hipchat', 'qrt_gplus',
'mid_other_chat', 'mid_phone', 'mid_facebook', 'mid_in_person',
'final_on_piazza', 'final_email', 'final_hipchat', 'qrt_gplus',
'final_other_chat', 'final_phone', 'final_facebook',
'final_in_person'
],
"Lecture Amount": [
'total_lecture_time', 'overal_lecture_views', 'lecture_1_views',
'lecture_2_views', 'lecture_3_views', 'lecture_4_views',
'lecture_5_views', 'lecture_6_views', 'lecture_7_views',
'lecture_8_views', 'lecture_9_views', 'lecture_10_views',
'lecture_11_views', 'lecture_12_views', 'lecture_13_views',
'lecture_14_views', 'lecture_15_views', 'lecture_16_views',
'lecture_17_views', 'lecture_18_views', 'lecture_19_views',
'lecture_20_views', 'lecture_21_views', 'lecture_22_views',
'lecture_23_views', 'lecture_24_views', 'lecture_25_views',
'lecture_26_views'
],
"Prior Experience": [
'formal_class_prog_taken', 'C', 'C#', 'C++', 'Java', 'JavaScript',
'Lisp', 'Objective C', 'Perl', 'PHP', 'Python', 'Ruby', 'Shell',
'Swift', 'Visual Basic', 'Other (specify below)',
'years_programming', 'prior_omscs_classes_completed', 'occupation',
'highest_education', 'besides_KBAI_how_many_classes',
'moocs_completed_outside_OMSCS'
],
"Self Assesment": [
'qtr_proj1_confidence_neither confident nor unconfident',
'qtr_proj1_confidence_no answer',
'qtr_proj1_confidence_somewhat confident',
'qtr_proj1_confidence_somewhat unconfident',
'qtr_proj1_confidence_very confident',
'qtr_proj1_confidence_very unconfident',
'qtr_proj2_confidence_neither confident nor unconfident',
'qtr_proj2_confidence_no answer',
'qtr_proj2_confidence_somewhat confident',
'qtr_proj2_confidence_somewhat unconfident',
'qtr_proj2_confidence_very confident',
'qtr_proj2_confidence_very unconfident',
'mid_proj2_confidence_neither confident nor unconfident',
'mid_proj2_confidence_no answer',
'mid_proj2_confidence_somewhat confident',
'mid_proj2_confidence_somewhat unconfident',
'mid_proj2_confidence_very confident',
'mid_proj2_confidence_very unconfident',
'mid_proj3_confidence_neither confident nor unconfident',
'mid_proj3_confidence_no answer',
'mid_proj3_confidence_somewhat confident',
'mid_proj3_confidence_somewhat unconfident',
'mid_proj3_confidence_very confident',
'mid_proj3_confidence_very unconfident',
'final_proj3_confidence_neither confident nor unconfident',
'final_proj3_confidence_no answer',
'final_proj3_confidence_somewhat confident',
'final_proj3_confidence_somewhat unconfident',
'final_proj3_confidence_very confident',
'final_proj3_confidence_very unconfident'
]
}
#list the data sources
data_sources = [
"basic_data", "basic_data_only_finishers", "basic_data_clean_lecture",
"basic_data_piazza", "basic_data_piazza_only_finishers"
]
#create csv for results
with open(os.path.join(results_location, 'regression_results.csv'),
'wb') as output_file:
#establish the csv writer
writer = csv.writer(output_file, delimiter=',')
for data_source in data_sources:
print("\n\n------------------")
print("Data Set - %s" % data_source)
print("------------------")
#this section determines R^2 scores of the regressors
writer.writerow(["R^2 Scores"])
writer.writerow(["Dataset - %s" % data_source])
#load the data from the csv
header, data = data_work.load_data(
data_source + ".csv",
conversion_function=data_work.convert_survey_data,
max_records=None)
#create headings
writer.writerow([
"Feature", "Decision Tree", "Boosting", "Random Forest",
"Linear Regression", "Support Vector Machine", "",
"Feature Details"
])
#loop through all the feature set combos
for feature_set_name, select_columns in feature_dict.iteritems():
print("\n\n------------------")
print("Feature Set - %s" % feature_set_name)
print("------------------")
#get the data subset
header_subset, data_subset = data_work.select_data_columns(
header,
data,
column_names=['course_grade'] + select_columns)
#first run on the full set
#assumes first column is Y
X_train, X_test, y_train, y_test = data_work.divide_for_training(
data_subset)
#remove the label header
header_subset = header_subset[1:]
#scale the data
X_train, X_test = data_work.scale_features(X_train, X_test)
#test all to start
run_regressors(
X_train, y_train, X_test, y_test, header_subset, writer,
data_source + "-" + feature_set_name + " Linear")
#for degree in [2, 3, 4]:
for degree in [2]:
#convert to polynomials
poly = PolynomialFeatures(degree=degree)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.fit_transform(X_test)
#test all in poly
run_regressors(
X_train_poly, y_train, X_test_poly, y_test,
header_subset, writer, data_source + "-" +
feature_set_name + " Poly %i" % degree)
##test individually
#for i in range(0, X_train.shape[1]):
# run_regressors(X_train[:, i,np.newaxis], y_train, X_test[:, i,np.newaxis], y_test, header_subset[i + 1, np.newaxis], writer)
return
