added imcomplete sparkling water code

train_pysparkling_h2o_gbm.py

@@ -0,0 +1,83 @@
+from pysparkling import *
+from h2o.estimators.gbm import H2OGradientBoostingEstimator as GBM
+
+'''
+This link might be helpful
+http://learn.h2o.ai/content/tutorials/pysparkling/Chicago_Crime_Demo.html
+'''
+
+def remove_pandas_index_column(line):
+    line = str(line).split(",")
+    line = line[1:] # Remove Pandas index column
+    return line
+
+def contains_target(row):
+    answer = False
+    if len(set(['Up','Left','Right']).intersection(row)) > 0:
+        answer = True
+    return answer
+
+def make_float_predictors(old_line):
+    # Convert only predictors to floats. Do not convert target, which is last element
+    new_line = [float(str(x)) for x in old_line[:len(old_line)-1]]
+    # Append the target, which is a class (ie String) and not a float
+    new_line.append(old_line[-1])
+    return new_line
+
+
+# Create H2O context for use later
+hc = H2OContext(sc)
+
+# Pull the data from S3
+rdd = sc.textFile("s3n://self-driving-car/data/*/predictors_and_targets.csv")
+
+# Remove index column
+rdd = rdd.map(remove_pandas_index_column)
+
+# Remove the header rows, which are easy to find because they won't have target values
+rdd = rdd.filter(contains_target)
+
+# Convert predictor values from String to Float
+rdd = rdd.map(lambda line: make_float_predictors(line))
+
+# Create predictor column names
+column_names = [str(x) for x in list(range(230400))]
+
+# Add on the target column to make a complete list of column names
+column_names.append('target')
+
+# Use the programmatically-generated column names to make a dataframe
+df = rdd.toDF(column_names)
+
+# Optionally print the quanity of columns in your dataframe
+df.columns
+
+# Optionally print column data types. Note that Spark intelligentlly
+# identifies that the predictor columns are double because I had
+# made all of them rdd elements double (above). This saved me from
+# having to write really ugly Spark casting code
+df.schema.fields
+
+# Convert the Spark DataFrame to something that H2O can ingest
+df_h2o = hc.as_h2o_frame(df,"df_h2o")
+
+'''
+
+
+
+'''
+
+predictors = column_names[:-1]
+response = column_names[-1]
+
+ratios = [0.6,0.2]
+h2o_frame_splits = df_h2o.split_frame(ratios,seed=12345)
+train = h2o_frame_splits[0]
+train.frame_id = "Train"
+valid = h2o_frame_splits[2]
+valid.frame_id = "Validation"
+test = h2o_frame_splits[1]
+test.frame_id = "Test"
+
+model = GBM(ntrees=50,max_depth=6,learn_rate=0.1,distribution="multinomial")
+model.train(x=predictors,y=response,training_frame=train,validation_frame=valid)