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Model Evaluation
Note: Extract probability values using method found here - https://www.sparkitecture.io/machine-learning/model-saving-and-loading#remove-unnecessary-columns-from-the-scored-data.
performance_df = spark.createDataFrame([(0,0,0)], ['cutoff', 'AUPR', 'AUC'])
for cutoff in range(5, 95, 5):
cutoff = (cutoff * 0.01)
print('Testing cutoff = ', str(format(cutoff, '.2f')))
lrpredictions_prob_temp = lrpredictions.withColumn('prediction_test', when(col('probability') >= cutoff, 1).otherwise(0).cast(DoubleType()))
aupr_temp = BinaryClassificationMetrics(lrpredictions_prob_temp['label', 'prediction_test'].rdd).areaUnderPR
auc_temp = BinaryClassificationMetrics(lrpredictions_prob_temp['label', 'prediction_test'].rdd).areaUnderROC
print('\tAUPR:', aupr_temp,'\tAUC:', auc_temp)
performance_df_row = spark.createDataFrame([(cutoff,aupr_temp,auc_temp)], ['cutoff', 'AUPR', 'AUC'])
performance_df = performance_df.union(performance_df_row)
display(performance_df)
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.mllib.evaluation import MulticlassMetrics
# Evaluate best model
print('Accuracy:', lrevaluator.evaluate(lrpredictions))
lrmetrics = MulticlassMetrics(lrpredictions['label','prediction'].rdd)
print('Confusion Matrix:\n', lrmetrics.confusionMatrix())
print('F1 Score:', lrmetrics.fMeasure(1.0,1.0))
for model in ["lrpredictions", "dtpredictions", "rfpredictions", "nbpredictions", "gbpredictions"]:
df = globals()[model]
tp = df[(df.label == 1) & (df.prediction == 1)].count()
tn = df[(df.label == 0) & (df.prediction == 0)].count()
fp = df[(df.label == 0) & (df.prediction == 1)].count()
fn = df[(df.label == 1) & (df.prediction == 0)].count()
a = ((tp + tn)/df.count())
if(tp + fn == 0.0):
r = 0.0
p = float(tp) / (tp + fp)
elif(tp + fp == 0.0):
r = float(tp) / (tp + fn)
p = 0.0
else:
r = float(tp) / (tp + fn)
p = float(tp) / (tp + fp)
if(p + r == 0):
f1 = 0
else:
f1 = 2 * ((p * r)/(p + r))
print("Model:", model)
print("True Positives:", tp)
print("True Negatives:", tn)
print("False Positives:", fp)
print("False Negatives:", fn)
print("Total:", df.count())
print("Accuracy:", a)
print("Recall:", r)
print("Precision: ", p)
print("F1 score:", f1)
print('AUC:', BinaryClassificationMetrics(df['label','prediction'].rdd).areaUnderROC)
print("\n")