Training a Random Forest on Tensorflow - python-3.x

Question

I am trying to train a tensorflow based random forest regression on numerical and continuos data.
When I try to fit my estimator it begins with the message below:
INFO:tensorflow:Constructing forest with params =
INFO:tensorflow:{'num_trees': 10, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 10, 'max_fertile_nodes': 0, 'split_after_samples': 250, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'model_name': 'all_dense', 'split_finish_name': 'basic', 'split_pruning_name': 'none', 'collate_examples': False, 'checkpoint_stats': False, 'use_running_stats_method': False, 'initialize_average_splits': False, 'inference_tree_paths': False, 'param_file': None, 'split_name': 'less_or_equal', 'early_finish_check_every_samples': 0, 'prune_every_samples': 0, 'feature_columns': [_NumericColumn(key='Average_Score', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), _NumericColumn(key='lat', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), _NumericColumn(key='lng', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)], 'num_classes': 1, 'num_features': 2, 'regression': True, 'bagged_num_features': 2, 'bagged_features': None, 'num_outputs': 1, 'num_output_columns': 2, 'base_random_seed': 0, 'leaf_model_type': 2, 'stats_model_type': 2, 'finish_type': 0, 'pruning_type': 0, 'split_type': 0}
Then the process breaks down and I get a value error below:
ValueError: Shape must be at least rank 2 but is rank 1 for 'concat' (op: 'ConcatV2') with input shapes: [?], [?], [?], [] and with computed input tensors: input[3] = <1>.
This is the code I am using:
import tensorflow as tf
from tensorflow.contrib.tensor_forest.python import tensor_forest
from tensorflow.python.ops import resources
import pandas as pd
from tensorflow.contrib.tensor_forest.client import random_forest
from tensorflow.python.estimator.inputs import numpy_io
import numpy as np
def getFeatures():
Average_Score = tf.feature_column.numeric_column('Average_Score')
lat = tf.feature_column.numeric_column('lat')
lng = tf.feature_column.numeric_column('lng')
return [Average_Score,lat ,lng]
# Import hotel data
Hotel_Reviews=pd.read_csv("./DataMining/Hotel_Reviews.csv")
Hotel_Reviews_Filtered=Hotel_Reviews[(Hotel_Reviews.lat.notnull() |
Hotel_Reviews.lng.notnull())]
Hotel_Reviews_Filtered_Target = Hotel_Reviews_Filtered[["Reviewer_Score"]]
Hotel_Reviews_Filtered_Features = Hotel_Reviews_Filtered[["Average_Score","lat","lng"]]
#Preprocess the data
x=Hotel_Reviews_Filtered_Features.to_dict('list')
for key in x:
x[key] = np.array(x[key])
y=Hotel_Reviews_Filtered_Target.values
#specify params
params = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(
feature_colums= getFeatures(),
num_classes=1,
num_features=2,
regression=True,
num_trees=10,
max_nodes=1000)
#build the graph
graph_builder_class = tensor_forest.RandomForestGraphs
est=random_forest.TensorForestEstimator(
params, graph_builder_class=graph_builder_class)
#define input function
train_input_fn = numpy_io.numpy_input_fn(
x=x,
y=y,
batch_size=1000,
num_epochs=1,
shuffle=True)
est.fit(input_fn=train_input_fn, steps=500)
The variables x is a list of numpy array of shape (512470,):
{'Average_Score': array([ 7.7, 7.7, 7.7, ..., 8.1, 8.1, 8.1]),
'lat': array([ 52.3605759, 52.3605759, 52.3605759, ..., 48.2037451,
48.2037451, 48.2037451]),
'lng': array([ 4.9159683, 4.9159683, 4.9159683, ..., 16.3356767,
16.3356767, 16.3356767])}
The variable y is numpy array of shape (512470,1):
array([[ 2.9],
[ 7.5],
[ 7.1],
...,
[ 2.5],
[ 8.8],
[ 8.3]])

Answer 1

Force each array in x to be 2 dim using ndmin=2. Then the shapes should match and concat should be able to operate.