Commit 1ff85c2e authored by Janez's avatar Janez

Merge branch 'totrtale_multiproc' of /home/git/repositories/kt/mothra

parents 8a2420d3 18152626
......@@ -17,3 +17,4 @@ liac-arff==2.0.1
networkx==1.9.1
djangorestframework==3.0.3
django-filter==0.9.1
discomll
......@@ -75,7 +75,7 @@ def dt_fit(input_dict):
random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
fitmodel_url = forest_distributed_decision_trees.fit(input = input_dict["dataset"],
fitmodel_url = forest_distributed_decision_trees.fit(input_dict["dataset"],
trees_per_chunk = input_dict["trees_per_subset"],
max_tree_nodes = input_dict["tree_nodes"],
min_samples_leaf = input_dict["min_samples_leaf"],
......@@ -102,7 +102,7 @@ def rf_fit(input_dict):
random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
fitmodel_url = distributed_random_forest.fit(input = input_dict["dataset"],
fitmodel_url = distributed_random_forest.fit(input_dict["dataset"],
trees_per_chunk = input_dict["trees_per_subset"],
max_tree_nodes = input_dict["tree_nodes"],
min_samples_leaf = input_dict["min_samples_leaf"],
......@@ -121,7 +121,7 @@ def rf_predict(input_dict):
random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
predictions_url = distributed_random_forest.predict(input = input_dict["dataset"],
predictions_url = distributed_random_forest.predict(input_dict["dataset"],
fitmodel_url = input_dict["fitmodel_url"],
random_state = random_state,
save_results = True)
......@@ -132,7 +132,7 @@ def wrf_fit(input_dict):
random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])
fitmodel_url = distributed_weighted_forest_rand.fit(input = input_dict["dataset"],
fitmodel_url = distributed_weighted_forest_rand.fit(input_dict["dataset"],
trees_per_chunk = input_dict["trees_per_subset"],
max_tree_nodes = input_dict["tree_nodes"],
num_medoids = input_dict["num_medoids"],
......@@ -149,7 +149,7 @@ def wrf_fit(input_dict):
def wrf_predict(input_dict):
from discomll.ensemble import distributed_weighted_forest_rand
predictions_url = distributed_weighted_forest_rand.predict(input = input_dict["dataset"],
predictions_url = distributed_weighted_forest_rand.predict(input_dict["dataset"],
fitmodel_url = input_dict["fitmodel_url"],
coeff = input_dict["coeff"],
save_results = True)
......@@ -236,7 +236,7 @@ def naivebayes_predict(input_dict):
from discomll.classification import naivebayes
m = 1 if input_dict["m"] == "" else input_dict["m"]
predictions_url = naivebayes.predict(input = input_dict["dataset"],
predictions_url = naivebayes.predict(input_dict["dataset"],
fitmodel_url = input_dict["fitmodel_url"],
m = input_dict["m"],
save_results = True )
......
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