def calculate_py_output(test_name,
factors,
method,
rotation,
top_dir=None):
"""
Use the `FactorAnalyzer()` class to perform the factor analysis
and return a dictionary with relevant output for given scenario.
Parameters
----------
test_name : str
The name of the test
factors : int
The number of factors
method : str
The rotation method
rotation : str
The type of rotation
top_dir : str, optional
The top directory for test data
Defaults to `DATA_DIR``
Returns
-------
output : dict
A dictionary containing the outputs
for all `OUTPUT_TYPES`.
"""
if top_dir is None:
top_dir = DATA_DIR
filename = join(top_dir, test_name + '.csv')
data = pd.read_csv(filename)
rotation = None if rotation == 'none' else rotation
method = {'uls': 'minres'}.get(method, method)
fa = FactorAnalyzer()
fa.analyze(data, factors, method=method, rotation=rotation)
evalues, values = fa.get_eigenvalues()
return {'value': values,
'evalues': evalues,
'structure': fa.structure,
'loading': fa.loadings,
'uniquenesses': fa.get_uniqueness(),
'communalities': fa.get_communalities(),
'scores': fa.get_scores(data)}
# In[20]:
from factor_analyzer import FactorAnalyzer
fa = FactorAnalyzer()
# In[28]:
fa.analyze(train, 3, rotation=None)
# In[29]:
fa.loadings
# In[30]:
fa.get_uniqueness()
# we have to select which feature have the heighest uniqueness value that feature is the first importent variable
# ## Principle component Analysis
# In[31]:
from sklearn.decomposition import PCA
pca = PCA(n_components=4)
principalComponents = pca.fit_transform(df)
principalDf = pd.DataFrame(data=principalComponents,
columns=[
'principal component 1',
'principal component 2',
'principal component 3', 'principal component 4'
])
[57, 46, 54, 46, 42], [38, 42, 41, 36, 41],
[43, 47, 41, 53, 44], [45, 51, 53, 46, 53],
[49, 56, 54, 61, 51], [35, 38, 57, 65, 57]])
seiseki_in = pd.DataFrame(seiseki_a, columns=subject)
seiseki = pd.DataFrame(scale(seiseki_in), columns=seiseki_in.columns.values)
fa = FactorAnalyzer()
fa.analyze(seiseki, 2, rotation="varimax")
#fa.analyze(seiseki, 2, rotation="promax")
#fa.analyze(seiseki, 2, rotation=None)
print('相関行列\n', seiseki.corr(method='pearson'))
print()
print('因子負荷量', fa.loadings.round(4)) # loadings
print()
print('独自性', fa.get_uniqueness().round(4)) # uniqueness
print()
print('因子分散', fa.get_factor_variance().round(4))
print()
##################
#寄与率
kiyo = np.array([0, 0])
for i in range(len(fa.loadings)):
u = np.array(fa.loadings.iloc[i])
kiyo = kiyo + u * u
kiyo = pd.DataFrame(kiyo / len(fa.loadings),
index=fa.loadings.columns.values).T
kiyo = kiyo.append(pd.DataFrame(np.cumsum(kiyo, axis=1)),
ignore_index=True).rename({
0: '寄与率',
