The following code is a detailed demonstration on how to run GridSearchCV on the NIML model using a Base Estimator wrapper. Since GridSearch is an exhaustive search means, it is suggested to be used in a situation where a good model is already found and improvements on it are theorized to occur with a specific set of parameters.
import csv import json import numpy as np from niml.encoder import encoder from pooler_module import Pooler from sklearn.neighbors import KNeighborsClassifier from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, ShuffleSplit import pandas as pd from sklearn.utils.estimator_checks import check_estimator import time import os
If you do not already have train and test dataset, load in the desired dataset and split it into a train and test subsets
def load_1hat_cvs_dataset(ds_filename, dup=0, rs=592): with open(ds_filename, 'r') as raw_data_fh: raw_data = list(csv.reader(raw_data_fh, delimiter = ',')) full_data = [] for line in raw_data: this_obs = [line[0]] this_obs.extend(list(map(float, line[1:]))) full_data.append(this_obs) extended_ds = full_data for x in range(dup): extended_ds.extend(full_data) train,test = train_test_split(extended_ds, test_size=0.20, random_state=rs) return train,test,full_data train,test,full_data = load_1hat_cvs_dataset('wbc_sklearn_labeled_data.csv')
Construct a default pooler object that will be used by the estimator. A Base Estimator requires default values for every variable, some of these values will be overwritten given the hyperparameters you provide.
estimator_p = Pooler( # Encoder/Data parameters sdr_width=784, enc_set_bits=4, enc_sparsity=0.25, enc_missing_val_ind = "?", tts_test_size = 0.20, #Set encoding train_test_split test size tts_random_state = np.random.RandomState(seed=592), #Set encoding train_test_split random_state # NPU neurons=500, active_neurons=5, input_pct=0.75, seed= 123, #Typically the arg seed synapse_inc=5, synapse_dec=1, activity_reset_cnt=50, decay_cnt_target=10, learning=True, # Boosting boost_max=0.9, boost_str=2, boost_tbl_size=21, boost_tbl_step=0.03, # Classifier classifier_ = KNeighborsClassifier(n_neighbors=3), # Set the classifier )
Define the type of scoring you would like to use, as well as number of cross-validation jobs, total searches, number of jobs to run in parallel, and whether or not you'd like to training scores to be included in the resulting CSV.
SCORING = 'f1_weighted' NUM_CV_JOBS = 2 #Default is 2 NUM_SEARCHES = 1 #Default is 1 NUM_PARALLEL_JOBS = 1 #Default is all (-1) TRAIN = True # whether to include training scores or not
Define the dictionary of hyperparameters over which you would like to perfom a Grid Search. Since this search is exhaustive, the smaller you keep this dictionary the less time it will take for the search to complete.
param_dis = { 'neurons': [1000], 'active_neurons' : [500, 600, 700, 1100], 'input_pct': [0.25], 'synapse_inc': [17], 'synapse_dec': [9], 'activity_reset_cnt': [50], 'enc_set_bits': [3, 5, 7], 'enc_sparsity': [0.5], 'boost_max': [0.9], 'boost_str': [1], 'boost_tbl_size': [21], 'boost_tbl_step': [0.66], }
Declare the search model you would like to use and pass it the Pooler object created before as the estimator, the desired parameter dictionary, the desired scoring, how many cross-validaiton jobs to perfom, how many parallel jobs to run, whether or not you would like to return the results of the train dataset, and how many messages you would like print as searches are completed.
search = GridSearchCV( estimator=estimator_p, param_grid=param_dis, scoring=SCORING, cv=NUM_CV_JOBS, n_jobs=NUM_PARALLEL_JOBS, return_train_score=TRAIN, verbose=3 ) print("Grid search - %d searches x %d cross validations" % (NUM_SEARCHES, NUM_CV_JOBS))
Grid search - 1 searches x 2 cross validations
Configure the train and test data and encode them.
pd_df_train = pd.DataFrame(train) pd_df_test = pd.DataFrame(test) estimator_p._get_dtypes() estimator_p._configure_encoder_params() estimator_p.encoder_ = estimator_p._create_encoder(full_data) # Use full dataset as the _create_encoder performs a train_test_split and configs encoder on the train split train_labels, train_isdrs, sdr_width = estimator_p.encoder_.encode(input_data=pd_df_train.iloc[:, 1:], label_col=None) test_labels, test_isdrs, sdr_width = estimator_p.encoder_.encode(input_data=pd_df_test.iloc[:, 1:], label_col=None) # Flatten and convert into integers train_labels = [int(train[index][0]) for index in range(len(train))] # Flatten and convert into integers test_labels = [int(test[index][0]) for index in range(len(test))] df = pd.DataFrame()
Determine how many time you would like to perform a Grid Search. Only one run is necessary as each combination of parameters will be tested.
runs = 1 # Total number of runs to perfrom (Default 1)
Declare the name of the resulting pooler state files (JSON and binary) and CSV you would like to save.
pooler_file = "JupyterNB_Grid" csv_file = "JupyterNB_Grid"
Define what parameters will be saved in the CSV file.
params = ['sdr_width', 'neurons', 'active_neurons', 'input_pct', 'seed', 'synapse_inc', 'synapse_dec', 'activity_reset_cnt', 'decay_cnt_target', 'feat_count', 'boost_max', 'boost_str', 'boost_tbl_size', 'boost_tbl_step', 'enc_set_bits', 'enc_sparsity', 'enc_missing_val_ind', 'learning']
Perform as many runs as declared previosuly by running a fit on the train data and searching for the best scoring Base Estimator.
best_score = 0 best_score_params = [] run_count = 0 while (run_count < runs): print("#############################################") print("Run %d" % run_count) search.fit(train_isdrs,train_labels) clf = search.best_estimator_ print("params:", search.best_estimator_.get_params()) print("score:", search.best_score_) run_count = run_count + 1 ### Keep track of the best overall score if search.best_score_ >= best_score: best_score = search.best_score_ best_score_params = search.best_estimator_.get_params() search.best_estimator_.save_pooler("%s_pooler.bin" % pooler_file, format="bin") search.best_estimator_.save_pooler("%s_pooler.json" % pooler_file) ## Save the CSV after each run, so no data is lost clf_params = clf.get_params() defaults = {} for param in params: if param in clf_params: defaults[f'param_{param}'] = clf_params[param] df_ap = pd.DataFrame(search.cv_results_) for param, val in defaults.items(): if param not in df.columns: df[params] = val df = df.append(df_ap, ignore_index=True) name = csv_file + "_RUNNING.csv" df.to_csv("%s" % name) # Save the CSV in case of the user cancelling a run, or error print("Saved running file %s" % name)
#############################################
Run 0
Fitting 2 folds for each of 12 candidates, totalling 24 fits
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.969, test=0.943), total= 10.9s
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 11.3s remaining: 0.0s
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.942), total= 10.3s
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 22.0s remaining: 0.0s
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.943), total= 10.6s
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.947), total= 10.8s
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.943), total= 10.8s
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=500, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.947), total= 10.9s
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.969, test=0.943), total= 11.9s
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.969, test=0.956), total= 11.4s
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.973, test=0.952), total= 12.1s
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.951), total= 12.2s
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.973, test=0.952), total= 12.1s
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=600, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.951), total= 11.7s
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.969, test=0.943), total= 13.6s
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.956), total= 13.7s
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.969, test=0.943), total= 13.5s
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.956), total= 13.5s
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.969, test=0.943), total= 12.6s
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=700, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=0.965, test=0.956), total= 13.4s
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=nan, test=nan), total= 0.0s
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=3, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=nan, test=nan), total= 0.0s
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=nan, test=nan), total= 0.0s
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=5, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=nan, test=nan), total= 0.0s
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=nan, test=nan), total= 0.0s
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17
[CV] active_neurons=1100, activity_reset_cnt=50, boost_max=0.9, boost_str=1, boost_tbl_size=21, boost_tbl_step=0.66, enc_set_bits=7, enc_sparsity=0.5, input_pct=0.25, neurons=1000, synapse_dec=9, synapse_inc=17, score=(train=nan, test=nan), total= 0.0s
Exception ignored in: <bound method SpatialPooler.__del__ of <niml.model.nispooler.sp_pooler_c.SpatialPooler object at 0x7f2a36867fd0>>
Traceback (most recent call last):
File "niml/model/nispooler/sp_pooler_c.py", line 189, in niml.model.nispooler.sp_pooler_c.SpatialPooler.__del__
TypeError: delete_pooler(): incompatible function arguments. The following argument types are supported:
1. (handle: niml.model.nispooler.sp_cpooler.Pooler) -> bool
Invoked with: None
/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/mpeterson/Skopt_fix/scikit-optimize/Skiml_DSA/pooler_module/pooler_estimator.py", line 265, in fit
self.pooler_ = self._create_pooler(self.sdr_width)
File "/home/mpeterson/Skopt_fix/scikit-optimize/Skiml_DSA/pooler_module/pooler_estimator.py", line 503, in _create_pooler
boost_tbl_size=self.boost_tbl_size,
File "niml/model/nispooler/sp_pooler_c.py", line 168, in niml.model.nispooler.sp_pooler_c.SpatialPooler.__init__
File "niml/model/nispooler/sp_pooler_c.py", line 404, in niml.model.nispooler.sp_pooler_c.SpatialPooler.set_properties
File "niml/model/nispooler/sp_pooler_c.py", line 402, in niml.model.nispooler.sp_pooler_c.SpatialPooler.set_properties
File "niml/model/nispooler/sp_pooler_c.py", line 465, in niml.model.nispooler.sp_pooler_c.SpatialPooler.validate_neuron_settings
ValueError: Value for active_neurons parameter (1100) cannot be larger than the value for neurons parameter (1000).
FitFailedWarning)
Exception ignored in: <bound method SpatialPooler.__del__ of <niml.model.nispooler.sp_pooler_c.SpatialPooler object at 0x7f2a36867fd0>>
Traceback (most recent call last):
File "niml/model/nispooler/sp_pooler_c.py", line 189, in niml.model.nispooler.sp_pooler_c.SpatialPooler.__del__
TypeError: delete_pooler(): incompatible function arguments. The following argument types are supported:
1. (handle: niml.model.nispooler.sp_cpooler.Pooler) -> bool
Invoked with: None
Exception ignored in: <bound method SpatialPooler.__del__ of <niml.model.nispooler.sp_pooler_c.SpatialPooler object at 0x7f2a368672e8>>
Traceback (most recent call last):
File "niml/model/nispooler/sp_pooler_c.py", line 189, in niml.model.nispooler.sp_pooler_c.SpatialPooler.__del__
TypeError: delete_pooler(): incompatible function arguments. The following argument types are supported:
1. (handle: niml.model.nispooler.sp_cpooler.Pooler) -> bool
Invoked with: None
Exception ignored in: <bound method SpatialPooler.__del__ of <niml.model.nispooler.sp_pooler_c.SpatialPooler object at 0x7f2a365d1eb8>>
Traceback (most recent call last):
File "niml/model/nispooler/sp_pooler_c.py", line 189, in niml.model.nispooler.sp_pooler_c.SpatialPooler.__del__
TypeError: delete_pooler(): incompatible function arguments. The following argument types are supported:
1. (handle: niml.model.nispooler.sp_cpooler.Pooler) -> bool
Invoked with: None
Exception ignored in: <bound method SpatialPooler.__del__ of <niml.model.nispooler.sp_pooler_c.SpatialPooler object at 0x7f2a365d1eb8>>
Traceback (most recent call last):
File "niml/model/nispooler/sp_pooler_c.py", line 189, in niml.model.nispooler.sp_pooler_c.SpatialPooler.__del__
TypeError: delete_pooler(): incompatible function arguments. The following argument types are supported:
1. (handle: niml.model.nispooler.sp_cpooler.Pooler) -> bool
Invoked with: None
Exception ignored in: <bound method SpatialPooler.__del__ of <niml.model.nispooler.sp_pooler_c.SpatialPooler object at 0x7f2a365d1eb8>>
Traceback (most recent call last):
File "niml/model/nispooler/sp_pooler_c.py", line 189, in niml.model.nispooler.sp_pooler_c.SpatialPooler.__del__
TypeError: delete_pooler(): incompatible function arguments. The following argument types are supported:
1. (handle: niml.model.nispooler.sp_cpooler.Pooler) -> bool
Invoked with: None
[Parallel(n_jobs=1)]: Done 24 out of 24 | elapsed: 3.7min finished
params: {'sdr_width': 784, 'neurons': 1000, 'active_neurons': 600, 'input_pct': 0.25, 'seed': 123, 'synapse_inc': 17, 'synapse_dec': 9, 'activity_reset_cnt': 50, 'decay_cnt_target': 10, 'learning': False, 'feat_count': 30, 'boost_max': 0.9, 'boost_str': 1, 'boost_tbl_size': 21, 'boost_tbl_step': 0.66, 'enc_set_bits': 5, 'enc_sparsity': 0.5}
score: 0.9513156255364192
Saved running file JupyterNB_Grid_RUNNING.csv
Print the highest scoring fit and the parameters that achieved it.
print("#############################################") print("Best score after %d runs: %f" % (run_count, best_score)) print("Best parameters: ", best_score_params) print("#############################################")
############################################# Best score aft