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"""module contains pca implementation using python"""
from typing import Any
from scipy import stats
from sklearn.decomposition import PCA
from sklearn import preprocessing
import numpy as np
import redis
from typing_extensions import TypeAlias
fArray: TypeAlias = list[float]
def compute_pca(array: list[fArray]) -> dict[str, Any]:
"""
computes the principal component analysis
Parameters:
array(list[list]):a list of lists contains data to perform pca
Returns:
pca_dict(dict):dict contains the pca_object,pca components,pca scores
"""
corr_matrix = np.array(array)
pca_obj = PCA()
scaled_data = preprocessing.scale(corr_matrix)
pca_obj.fit(scaled_data)
return {
"pca": pca_obj,
"components": pca_obj.components_,
"scores": pca_obj.transform(scaled_data)
}
def generate_scree_plot_data(variance_ratio: fArray) -> tuple[list, fArray]:
"""
generates the scree data for plotting
Parameters:
variance_ratio(list[floats]):ratios for contribution of each pca
Returns:
coordinates(list[(x_coor,y_coord)])
"""
perc_var = [round(ratio*100, 1) for ratio in variance_ratio]
x_coordinates = [f"PC{val}" for val in range(1, len(perc_var)+1)]
return (x_coordinates, perc_var)
def generate_pca_traits_vals(trait_data_array: list[fArray],
corr_array: list[fArray]) -> list[list[Any]]:
"""
generates datasets from zscores of the traits and eigen_vectors\
of correlation matrix
Parameters:
trait_data_array(list[floats]):an list of the traits
corr_array(list[list]): list of arrays for computing eigen_vectors
Returns:
pca_vals[list[list]]:
"""
trait_zscores = stats.zscore(trait_data_array)
if len(trait_data_array[0]) < 10:
trait_zscores = trait_data_array
(eigen_values, corr_eigen_vectors) = np.linalg.eig(np.array(corr_array))
idx = eigen_values.argsort()[::-1]
return np.dot(corr_eigen_vectors[:, idx], trait_zscores)
def process_factor_loadings_tdata(factor_loadings, traits_num: int):
"""
transform loadings for tables visualization
Parameters:
factor_loading(numpy.ndarray)
traits_num(int):number of traits
Returns:
tabular_loadings(list[list[float]])
"""
target_columns = 3 if traits_num > 2 else 2
trait_loadings = list(factor_loadings.T)
return [list(trait_loading[:target_columns])
for trait_loading in trait_loadings]
def generate_pca_temp_traits(
species: str,
group: str,
traits_data: list[fArray],
corr_array: list[fArray],
dataset_samples: list[str],
shared_samples: list[str],
create_time: str
) -> dict[str, list[Any]]:
"""
generate pca temp datasets
"""
# pylint: disable=too-many-arguments
pca_trait_dict = {}
pca_vals = generate_pca_traits_vals(traits_data, corr_array)
for (idx, pca_trait) in enumerate(list(pca_vals)):
trait_id = f"PCA{str(idx+1)}_{species}_{group}_{create_time}"
sample_vals = []
pointer = 0
for sample in dataset_samples:
if sample in shared_samples:
sample_vals.append(str(pca_trait[pointer]))
pointer += 1
else:
sample_vals.append("x")
pca_trait_dict[trait_id] = sample_vals
return pca_trait_dict
def cache_pca_dataset(redis_conn: Any, exp_days: int,
pca_trait_dict: dict[str, list[Any]]):
"""
caches pca dataset to redis
Parameters:
redis_conn(object)
exp_days(int): fo redis cache
pca_trait_dict(Dict): contains traits and traits vals to cache
Returns:
boolean(True if correct conn object False incase of exception)
"""
try:
for trait_id, sample_data in pca_trait_dict.items():
samples_str = " ".join([str(x) for x in sample_data])
redis_conn.set(trait_id, samples_str, ex=exp_days)
return True
except (redis.ConnectionError, AttributeError):
return False
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