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optimal_k_finder.py
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optimal_k_finder.py
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import numpy as np
from pyspark.ml.clustering import BisectingKMeans, KMeans, KMeansModel, BisectingKMeansModel, GaussianMixture, \
GaussianMixtureModel
from pyspark.sql import DataFrame
from data_preparer import DataPreparer
from evaluators import ElbowEvaluator, SilhouetteEvaluator, EvaluationResult
class OptimalKSeedFinder:
"""
For details in Finding Optimal K and Seeds:
See #1: https://medium.com/analytics-vidhya/how-to-determine-the-optimal-k-for-k-means-708505d204eb
See #2: https://linkedin.com/pulse/finding-optimal-number-clusters-k-means-through-elbow-asanka-perera/
"""
__DEF_K_MEANS_DISTANCE_NORM = 'euclidean'
# __DEF_K_MEANS_DISTANCE_NORM = 'cosine' # Alternative to 'euclidean'
__METHODS = ['Elbow', 'Silhouette']
__RAND_LIMIT = 1000
def __init__(self, df: DataFrame, ftr_col: str, prd_col: str, k_1: int, k_n: int, seed_try: int, verbose: bool):
self.check_k_values(k_1, k_n)
self.df = DataPreparer.clone_df(df) # type: DataFrame
self.ftr_col = ftr_col
self.prd_col = prd_col
self.k_start = k_1
self.k_end = k_n
self.seed_try = seed_try if (seed_try > 1) else 1 # less than 2 -> no seed try!
self.__verbose = verbose
@staticmethod
def check_k_values(k_1: int, k_n: int):
if not (1 < k_1 < k_n):
raise Exception('K values did not satisfy the condition: "(1 < k_1 < k_n)".')
if not isinstance(k_1, int) or not isinstance(k_n, int):
raise Exception('Not all k values are integer.')
@property
def k_range(self) -> range:
return range(self.k_start, self.k_end + 1)
def __generate_seeds(self) -> np.ndarray:
return np.random.randint(self.__RAND_LIMIT, size=self.seed_try)
def __adjust_k_range_for_elbow(self, elbow_evaluator: ElbowEvaluator) -> None:
self.k_end += 1 # k_end is incremented in order to make it maximum limit in elbow method.
if self.k_range.start == 2:
elbow_evaluator.add_cost_for_1_cluster() # 1 is not valid for pyspark k_means, so add cost, externally
else:
self.k_start -= 1 # k_end is decremented in order to make it minimum limit in elbow method.
def k_means_elbow(self, dist_measure: str = __DEF_K_MEANS_DISTANCE_NORM) -> EvaluationResult:
elbow_eval = ElbowEvaluator(self.df, self.ftr_col, self.prd_col, SilhouetteEvaluator.METRIC_SQUARED_EUCLIDEAN)
k_means = KMeans(featuresCol=self.ftr_col, predictionCol=self.prd_col, distanceMeasure=dist_measure)
self.__adjust_k_range_for_elbow(elbow_eval)
for k in self.k_range:
k_means.setK(k)
seeds = self.__generate_seeds()
for seed in seeds:
k_means.setSeed(seed)
k_means_model = k_means.fit(self.df) # type: KMeansModel
df_with_predictions = k_means_model.transform(self.df) # type:DataFrame
elbow_eval.add_k_means_cost(k_means_model, df_with_predictions, k, seed)
return elbow_eval.finalize(verbose=self.__verbose)
def bisecting_k_means_elbow(self, dist_measure: str = __DEF_K_MEANS_DISTANCE_NORM) -> EvaluationResult:
elbow_eval = ElbowEvaluator(self.df, self.ftr_col, self.prd_col, SilhouetteEvaluator.METRIC_SQUARED_EUCLIDEAN)
b_k_means = BisectingKMeans(featuresCol=self.ftr_col, predictionCol=self.prd_col, distanceMeasure=dist_measure)
self.__adjust_k_range_for_elbow(elbow_eval)
for k in self.k_range:
b_k_means.setK(k)
seeds = self.__generate_seeds()
for seed in seeds:
b_k_means.setSeed(seed)
bisect_k_means_model = b_k_means.fit(self.df) # type: BisectingKMeansModel
df_with_predictions = bisect_k_means_model.transform(self.df) # type:DataFrame
elbow_eval.add_bisecting_k_means_cost(bisect_k_means_model, df_with_predictions, k, seed)
return elbow_eval.finalize(verbose=self.__verbose)
def k_means_silhouette(self, dist_measure: str = __DEF_K_MEANS_DISTANCE_NORM) -> EvaluationResult:
slh_evaluator = SilhouetteEvaluator(self.ftr_col, self.prd_col, SilhouetteEvaluator.METRIC_SQUARED_EUCLIDEAN)
k_means = KMeans(featuresCol=self.ftr_col, predictionCol=self.prd_col, distanceMeasure=dist_measure)
for k in self.k_range:
k_means.setK(k)
seeds = self.__generate_seeds()
for seed in seeds:
k_means.setSeed(seed)
k_means_model = k_means.fit(self.df) # type: KMeansModel
df_with_predictions = k_means_model.transform(self.df) # type:DataFrame
slh_evaluator.calculate_add(data=df_with_predictions, k=k, seed=k_means.getSeed())
if self.__verbose:
k_th_silhouette = slh_evaluator.calculate(df_with_predictions)
print('K(#_OF_CLUSTER)=<%d>, SEED=<%d>' % (k, k_means.getSeed()))
print('INIT_MODE="%s", SILHOUETTE_SCORE=<%f>' % (k_means.getInitMode(), k_th_silhouette))
print('FINAL_CENTROIDS: ')
for i, centroid in enumerate(k_means_model.clusterCenters()):
print('> [{}] {}'.format(i + 1, centroid))
print('=' * 42)
return slh_evaluator.finalize()
def bisecting_k_means_silhouette(self, dist_measure: str = __DEF_K_MEANS_DISTANCE_NORM) -> EvaluationResult:
slh_evaluator = SilhouetteEvaluator(self.ftr_col, self.prd_col, SilhouetteEvaluator.METRIC_SQUARED_EUCLIDEAN)
b_k_means = BisectingKMeans(featuresCol=self.ftr_col, predictionCol=self.prd_col, distanceMeasure=dist_measure)
for k in self.k_range:
b_k_means.setK(k)
seeds = self.__generate_seeds()
for seed in seeds:
b_k_means.setSeed(seed)
b_k_means_model = b_k_means.fit(self.df) # type: BisectingKMeansModel
df_with_predictions = b_k_means_model.transform(self.df) # type:DataFrame
slh_evaluator.calculate_add(data=df_with_predictions, k=k, seed=b_k_means.getSeed())
if self.__verbose:
k_th_silhouette = slh_evaluator.calculate(df_with_predictions)
print('K(#_OF_CLUSTER)=<%d>, SEED=<%d>' % (k, b_k_means.getSeed()))
print('SILHOUETTE_SCORE=<%f>' % k_th_silhouette)
print('FINAL_CENTROIDS: ')
for i, centroid in enumerate(b_k_means_model.clusterCenters()):
print('> [{}] {}'.format(i + 1, centroid))
print('=' * 42)
return slh_evaluator.finalize()
def gm_silhouette(self, prb_col: str):
slh_evaluator = SilhouetteEvaluator(self.ftr_col, self.prd_col, SilhouetteEvaluator.METRIC_SQUARED_EUCLIDEAN)
gm = GaussianMixture(featuresCol=self.ftr_col, predictionCol=self.prd_col, probabilityCol=prb_col)
for k in self.k_range:
gm.setK(k)
seeds = self.__generate_seeds()
for seed in seeds:
gm.setSeed(seed)
gm_model = gm.fit(self.df) # type: GaussianMixtureModel
print(gm_model.summary.cluster.show(truncate=False, n=500))
print("LOG LIKELIHOOD:", gm_model.summary.logLikelihood)
print("=============================================")
print(gm_model.gaussiansDF.select('mean').show(truncate=False))
print(gm_model.gaussiansDF.select('cov').show(truncate=False))
print(gm_model.gaussiansDF.show(truncate=False))
print('WEIGHTS:', gm_model.weights)
print("=============================================")
exit(32)
df_with_predictions = gm_model.transform(self.df) # type:DataFrame
slh_evaluator.calculate_add(data=df_with_predictions, k=k, seed=gm.getSeed())
if self.__verbose:
k_th_silhouette = slh_evaluator.calculate(df_with_predictions)
print('K(#_OF_CLUSTER)=<%d>, SEED=<%d>' % (k, gm.getSeed()))
print('SILHOUETTE_SCORE=<%f>' % k_th_silhouette)
print('=' * 42)
print(slh_evaluator.results_to_str)
return slh_evaluator.finalize()