Confusion Matrix to get precsion,recall, f1score - python-3.x

Question

I have a dataframe df. I have performed decisionTree classification algorithm on the dataframe. The two columns are label and features when algorithm is performed. The model is called dtc. How can I create a confusion matrix in pyspark?
dtc = DecisionTreeClassifier(featuresCol = 'features', labelCol = 'label')
dtcModel = dtc.fit(train)
predictions = dtcModel.transform(test)
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.evaluation import MulticlassMetrics
preds = df.select(['label', 'features']) \
.df.map(lambda line: (line[1], line[0]))
metrics = MulticlassMetrics(preds)
# Confusion Matrix
print(metrics.confusionMatrix().toArray())```

Answer 1

You need to cast to an rdd and map to tuple before calling metrics.confusionMatrix().toArray().
From the official documentation,
class pyspark.mllib.evaluation.MulticlassMetrics(predictionAndLabels)[source]
Evaluator for multiclass classification.
Parameters: predictionAndLabels – an RDD of (prediction, label) pairs.
Here is an example to guide you.
ML part
import pyspark.sql.functions as F
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.mllib.evaluation import MulticlassMetrics
from pyspark.sql.types import FloatType
#Note the differences between ml and mllib, they are two different libraries.
#create a sample data frame
data = [(1.54,3.45,2.56,0),(9.39,8.31,1.34,0),(1.25,3.31,9.87,1),(9.35,5.67,2.49,2),\
(1.23,4.67,8.91,1),(3.56,9.08,7.45,2),(6.43,2.23,1.19,1),(7.89,5.32,9.08,2)]
cols = ('a','b','c','d')
df = spark.createDataFrame(data, cols)
assembler = VectorAssembler(inputCols=['a','b','c'], outputCol='features')
df_features = assembler.transform(df)
#df.show()
train_data, test_data = df_features.randomSplit([0.6,0.4])
dtc = DecisionTreeClassifier(featuresCol='features',labelCol='d')
dtcModel = dtc.fit(train_data)
predictions = dtcModel.transform(test_data)
Evaluation part
#important: need to cast to float type, and order by prediction, else it won't work
preds_and_labels = predictions.select(['predictions','d']).withColumn('label', F.col('d').cast(FloatType())).orderBy('prediction')
#select only prediction and label columns
preds_and_labels = preds_and_labels.select(['prediction','label'])
metrics = MulticlassMetrics(preds_and_labels.rdd.map(tuple))
print(metrics.confusionMatrix().toArray())

Answer 2

Use this:
import sklearn
from pyspark.ml.classification import RandomForestClassifier
rf = RandomForestClassifier(featuresCol = 'features', labelCol = 'label', numTrees=500)
rfModel = rf.fit(train)
predictions_train = rfModel.transform(train)
y_true = predictions_train.select(['label']).collect()
y_pred = predictions_train.select(['prediction']).collect()
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(y_true, y_pred))
where train is your training data.