diff options
| -rw-r--r-- | wqflask/wqflask/correlation_matrix/show_corr_matrix.py | 70 |
1 files changed, 6 insertions, 64 deletions
diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py index 9462f973..bcd73436 100644 --- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py +++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py @@ -41,6 +41,7 @@ from utility.redis_tools import get_redis_conn from gn3.computations.principal_component_analysis import compute_pca from gn3.computations.principal_component_analysis import process_factor_loadings_tdata +from gn3.computations.principal_component_analysis import generate_pca_traits_vals Redis = get_redis_conn() THIRTY_DAYS = 60 * 60 * 24 * 30 @@ -169,15 +170,12 @@ class CorrelationMatrix: self.pca_works = "False" try: - corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results)) - corr_eigen_value, corr_eigen_vectors = sortEigenVectors( - corr_result_eigen) if self.do_PCA == True: self.pca_works = "True" self.pca_trait_ids = [] pca = self.calculate_pca( - list(range(len(self.traits))), corr_eigen_value, corr_eigen_vectors) + list(range(len(self.traits)))) self.loadings_array = process_factor_loadings_tdata(self.loadings,len(self.trait_list)) else: self.pca_works = "False" @@ -191,7 +189,7 @@ class CorrelationMatrix: samples=self.all_sample_list, sample_data=self.sample_data,) - def calculate_pca(self, cols, corr_eigen_value, corr_eigen_vectors): + def calculate_pca(self, cols): pca = compute_pca(self.pca_corr_results) @@ -199,8 +197,9 @@ class CorrelationMatrix: self.loadings = pca["components"] self.scores = pca["scores"] - trait_array = zScore(self.trait_data_array) - trait_array_vectors = np.dot(corr_eigen_vectors, trait_array) + trait_array_vectors = generate_pca_traits_vals(self.trait_data_array,self.pca_corr_results) + + pca_traits = [] for i, vector in enumerate(trait_array_vectors): @@ -231,21 +230,6 @@ class CorrelationMatrix: return pca - def process_loadings(self): - loadings_array = [] - loadings_row = [] - for i in range(len(self.trait_list)): - loadings_row = [] - if len(self.trait_list) > 2: - the_range = 3 - else: - the_range = 2 - for j in range(the_range): - position = i + len(self.trait_list) * j - loadings_row.append(self.loadings[0][position]) - loadings_array.append(loadings_row) - return loadings_array - def export_corr_matrix(corr_results): corr_matrix_filename = "corr_matrix_" + \ @@ -285,45 +269,3 @@ def export_corr_matrix(corr_results): return corr_matrix_filename, matrix_export_path - -def zScore(trait_data_array): - NN = len(trait_data_array[0]) - if NN < 10: - return trait_data_array - else: - i = 0 - for data in trait_data_array: - N = len(data) - S = reduce(lambda x, y: x + y, data, 0.) - SS = reduce(lambda x, y: x + y * y, data, 0.) - mean = S / N - var = SS - S * S / N - stdev = math.sqrt(var / (N - 1)) - if stdev == 0: - stdev = 1e-100 - data2 = [(x - mean) / stdev for x in data] - trait_data_array[i] = data2 - i += 1 - return trait_data_array - - -def sortEigenVectors(vector): - try: - eigenValues = vector[0].tolist() - eigenVectors = vector[1].T.tolist() - combines = [] - i = 0 - for item in eigenValues: - combines.append([eigenValues[i], eigenVectors[i]]) - i += 1 - sorted(combines, key=cmp_to_key(webqtlUtil.cmpEigenValue)) - A = [] - B = [] - for item in combines: - A.append(item[0]) - B.append(item[1]) - sum = reduce(lambda x, y: x + y, A, 0.0) - A = [x * 100.0 / sum for x in A] - return [A, B] - except: - return [] |