Mercurial > repos > bgruening > sklearn_pca

annotate pca.py @ 7:c16818ce0424 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
author bgruening
date Wed, 09 Aug 2023 13:22:12 +0000
parents 132805688fa3
children
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1 import argparse
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2
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3 import numpy as np
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4 from galaxy_ml.utils import read_columns
1
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5 from sklearn.decomposition import IncrementalPCA, KernelPCA, PCA
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6
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7
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8 def main():
1
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9 parser = argparse.ArgumentParser(description="RDKit screen")
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10 parser.add_argument("-i", "--infile", help="Input file")
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11 parser.add_argument(
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12 "--header", action="store_true", help="Include the header row or skip it"
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13 )
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14 parser.add_argument(
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15 "-c",
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16 "--columns",
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17 type=str.lower,
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18 default="all",
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19 choices=[
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20 "by_index_number",
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21 "all_but_by_index_number",
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22 "by_header_name",
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23 "all_but_by_header_name",
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24 "all_columns",
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25 ],
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26 help="Choose to select all columns, or exclude/include some",
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27 )
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28 parser.add_argument(
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29 "-ci",
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30 "--column_indices",
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31 type=str.lower,
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32 help="Choose to select all columns, or exclude/include some",
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33 )
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34 parser.add_argument(
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35 "-n",
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36 "--number",
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37 nargs="?",
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38 type=int,
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39 default=None,
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40 help="Number of components to keep. If not set, all components are kept",
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41 )
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42 parser.add_argument("--whiten", action="store_true", help="Whiten the components")
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43 parser.add_argument(
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44 "-t",
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45 "--pca_type",
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46 type=str.lower,
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47 default="classical",
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48 choices=["classical", "incremental", "kernel"],
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49 help="Choose which flavour of PCA to use",
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50 )
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51 parser.add_argument(
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52 "-s",
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53 "--svd_solver",
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54 type=str.lower,
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55 default="auto",
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56 choices=["auto", "full", "arpack", "randomized"],
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57 help="Choose the type of svd solver.",
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58 )
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59 parser.add_argument(
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60 "-b",
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61 "--batch_size",
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62 nargs="?",
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63 type=int,
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64 default=None,
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65 help="The number of samples to use for each batch",
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66 )
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67 parser.add_argument(
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68 "-k",
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69 "--kernel",
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70 type=str.lower,
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71 default="linear",
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72 choices=["linear", "poly", "rbf", "sigmoid", "cosine", "precomputed"],
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73 help="Choose the type of kernel.",
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74 )
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75 parser.add_argument(
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76 "-g",
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77 "--gamma",
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78 nargs="?",
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79 type=float,
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80 default=None,
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81 help="Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels",
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82 )
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83 parser.add_argument(
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84 "-tol",
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85 "--tolerance",
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86 type=float,
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87 default=0.0,
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88 help="Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack",
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89 )
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90 parser.add_argument(
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91 "-mi",
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92 "--max_iter",
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93 nargs="?",
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94 type=int,
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95 default=None,
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96 help="Maximum number of iterations for arpack",
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97 )
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98 parser.add_argument(
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99 "-d",
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100 "--degree",
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101 type=int,
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102 default=3,
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103 help="Degree for poly kernels. Ignored by other kernels",
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104 )
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105 parser.add_argument(
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106 "-cf",
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107 "--coef0",
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108 type=float,
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109 default=1.0,
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110 help="Independent term in poly and sigmoid kernels",
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111 )
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112 parser.add_argument(
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113 "-e",
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114 "--eigen_solver",
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115 type=str.lower,
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116 default="auto",
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117 choices=["auto", "dense", "arpack"],
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118 help="Choose the type of eigen solver.",
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119 )
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120 parser.add_argument(
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121 "-o", "--outfile", help="Base name for output file (no extension)."
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122 )
0
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123 args = parser.parse_args()
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124
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125 usecols = None
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126 pca_params = {}
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127
1
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128 if args.columns == "by_index_number" or args.columns == "all_but_by_index_number":
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129 usecols = [int(i) for i in args.column_indices.split(",")]
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130 elif args.columns == "by_header_name" or args.columns == "all_but_by_header_name":
0
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131 usecols = args.column_indices
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132
1
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133 header = "infer" if args.header else None
0
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134
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135 pca_input = read_columns(
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136 f=args.infile,
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137 c=usecols,
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138 c_option=args.columns,
1
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139 sep="\t",
0
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140 header=header,
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141 parse_dates=True,
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142 encoding=None,
1
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143 index_col=None,
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144 )
0
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145
1
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146 pca_params.update({"n_components": args.number})
0
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147
1
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148 if args.pca_type == "classical":
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149 pca_params.update({"svd_solver": args.svd_solver, "whiten": args.whiten})
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150 if args.svd_solver == "arpack":
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151 pca_params.update({"tol": args.tolerance})
0
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152 pca = PCA()
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153
1
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154 elif args.pca_type == "incremental":
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155 pca_params.update({"batch_size": args.batch_size, "whiten": args.whiten})
0
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156 pca = IncrementalPCA()
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157
1
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158 elif args.pca_type == "kernel":
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159 pca_params.update(
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160 {
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161 "kernel": args.kernel,
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162 "eigen_solver": args.eigen_solver,
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163 "gamma": args.gamma,
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164 }
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165 )
0
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166
1
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167 if args.kernel == "poly":
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168 pca_params.update({"degree": args.degree, "coef0": args.coef0})
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169 elif args.kernel == "sigmoid":
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170 pca_params.update({"coef0": args.coef0})
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171 elif args.kernel == "precomputed":
0
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172 pca_input = np.dot(pca_input, pca_input.T)
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173
1
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174 if args.eigen_solver == "arpack":
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175 pca_params.update({"tol": args.tolerance, "max_iter": args.max_iter})
0
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176
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177 pca = KernelPCA()
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178
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179 print(pca_params)
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180 pca.set_params(**pca_params)
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181 pca_output = pca.fit_transform(pca_input)
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182 np.savetxt(fname=args.outfile, X=pca_output, fmt="%.4f", delimiter="\t")
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183
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184
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185 if __name__ == "__main__":
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186 main()