GrimAge2

GrimAge2#

Index#

  1. Instantiate model class

  2. Define clock metadata

  3. Download clock dependencies

  4. Load features

  5. Load weights into base model

  6. Load reference values

  7. Load preprocess and postprocess objects

  8. Check all clock parameters

  9. Basic test

  10. Save torch model

  11. Clear directory

Let’s first import some packages:

[1]:

import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya
import numpy as np

Instantiate model class#

[2]:

def print_entire_class(cls):
    source = inspect.getsource(cls)
    print(source)

print_entire_class(pya.models.GrimAge2)

class GrimAge(pyagingModel):
    def __init__(self):
        super().__init__()

        self.PACKYRS = None
        self.ADM = None
        self.B2M = None
        self.CystatinC = None
        self.GDF15 = None
        self.Leptin = None
        self.PAI1 = None
        self.TIMP1 = None

        self.features_PACKYRS = None
        self.features_ADM = None
        self.features_B2M = None
        self.features_CystatinC = None
        self.features_GDF15 = None
        self.features_Leptin = None
        self.features_PAI1 = None
        self.features_TIMP1 = None

    def forward(self, x):
        Female = x[:, -2].unsqueeze(1)
        Age = x[:, -1].unsqueeze(1)

        PACKYRS = self.PACKYRS(x[:, self.features_PACKYRS])
        ADM = self.ADM(x[:, self.features_ADM])
        B2M = self.B2M(x[:, self.features_B2M])
        CystatinC = self.CystatinC(x[:, self.features_CystatinC])
        GDF15 = self.GDF15(x[:, self.features_GDF15])
        Leptin = self.Leptin(x[:, self.features_Leptin])
        PAI1 = self.PAI1(x[:, self.features_PAI1])
        TIMP1 = self.TIMP1(x[:, self.features_TIMP1])

        x = torch.concat(
            [GDF15, B2M, CystatinC, TIMP1, ADM, PAI1, Leptin, PACKYRS, Age, Female],
            dim=1,
        )

        x = self.base_model(x)

        x = self.postprocess(x)

        return x

    def preprocess(self, x):
        return x

    def postprocess(self, x):
        """
        Converts from a Cox parameter to age in units of years.
        """
        cox_mean = 13.20127
        cox_std = 1.086805
        age_mean = 59.63951
        age_std = 9.049608

        # Normalize
        x = (x - cox_mean) / cox_std

        # Scale
        x = (x * age_std) + age_mean

        return x


[3]:

model = pya.models.GrimAge2()

Define clock metadata#

[4]:

model.metadata["clock_name"] = 'grimage2'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2022
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Lu, Ake T., et al. \"DNA methylation GrimAge version 2.\" Aging (Albany NY) 14.23 (2022): 9484."
model.metadata["doi"] = "https://doi.org/10.18632/aging.204434"
model.metadata["research_only"] = True
model.metadata["notes"] = None

Download clock dependencies#

[5]:

logger = pya.logger.Logger()
urls = [
    "https://pyaging.s3.amazonaws.com/supporting_files/grimage2_subcomponents.csv",
    "https://pyaging.s3.amazonaws.com/supporting_files/grimage2.csv",
    "https://pyaging.s3.amazonaws.com/supporting_files/datMiniAnnotation3_Gold.csv",
]
dir = "."
for url in urls:
    pya.utils.download(url, dir, logger, indent_level=1)

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Load features#

From CSV#

[6]:

df = pd.read_csv('grimage2_subcomponents.csv', index_col=0)
df_grimage = pd.read_csv('grimage2.csv', index_col=0)
model.features = np.unique(df['var']).tolist()[2:] + ['female'] + ['age']

Load weights into base model#

Linear model#

[7]:

all_features = np.unique(df['var']).tolist()[2:] + ['Female'] + ['Age']

model.PACKYRS = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmPACKYRS'])))
model.PACKYRS.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmPACKYRS', 'beta'][1:])).unsqueeze(0).float()
model.PACKYRS.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmPACKYRS', 'beta'].iloc[0])).float()
model.features_PACKYRS = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmPACKYRS', 'var']) if item in all_features]).long()

model.ADM = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmadm'])))
model.ADM.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmadm', 'beta'][1:])).unsqueeze(0).float()
model.ADM.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmadm', 'beta'].iloc[0])).float()
model.features_ADM = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmadm', 'var']) if item in all_features]).long()

model.B2M = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmB2M'])))
model.B2M.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmB2M', 'beta'][1:])).unsqueeze(0).float()
model.B2M.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmB2M', 'beta'].iloc[0])).float()
model.features_B2M = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmB2M', 'var']) if item in all_features]).long()

model.CystatinC = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmCystatin_C'])))
model.CystatinC.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmCystatin_C', 'beta'][1:])).unsqueeze(0).float()
model.CystatinC.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmCystatin_C', 'beta'].iloc[0])).float()
model.features_CystatinC = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmCystatin_C', 'var']) if item in all_features]).long()

model.GDF15 = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmGDF_15'])))
model.GDF15.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmGDF_15', 'beta'][1:])).unsqueeze(0).float()
model.GDF15.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmGDF_15', 'beta'].iloc[0])).float()
model.features_GDF15 = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmGDF_15', 'var']) if item in all_features]).long()

model.Leptin = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmleptin'])))
model.Leptin.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmleptin', 'beta'][1:])).unsqueeze(0).float()
model.Leptin.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmleptin', 'beta'].iloc[0])).float()
model.features_Leptin = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmleptin', 'var']) if item in all_features]).long()

model.PAI1 = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmpai_1'])))
model.PAI1.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmpai_1', 'beta'][1:])).unsqueeze(0).float()
model.PAI1.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmpai_1', 'beta'].iloc[0])).float()
model.features_PAI1 = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmpai_1', 'var']) if item in all_features]).long()

model.TIMP1 = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmTIMP_1'])))
model.TIMP1.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmTIMP_1', 'beta'][1:])).unsqueeze(0).float()
model.TIMP1.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmTIMP_1', 'beta'].iloc[0])).float()
model.features_TIMP1 = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmTIMP_1', 'var']) if item in all_features]).long()

model.LogCRP = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmlog.CRP'])))
model.LogCRP.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmlog.CRP', 'beta'][1:])).unsqueeze(0).float()
model.LogCRP.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmlog.CRP', 'beta'].iloc[0])).float()
model.features_LogCRP = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmlog.CRP', 'var']) if item in all_features]).long()

model.A1C = pya.models.LinearModel(input_dim=len(np.array(df.loc[df['Y.pred'] == 'DNAmlog.A1C'])))
model.A1C.linear.weight.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmlog.A1C', 'beta'][1:])).unsqueeze(0).float()
model.A1C.linear.bias.data = torch.tensor(np.array(df.loc[df['Y.pred'] == 'DNAmlog.A1C', 'beta'].iloc[0])).float()
model.features_A1C = indices = torch.tensor([all_features.index(item) for item in np.array(df.loc[df['Y.pred'] == 'DNAmlog.A1C', 'var']) if item in all_features]).long()

Linear model#

[8]:

base_model = pya.models.LinearModel(input_dim=len(df_grimage))

base_model.linear.weight.data = torch.tensor(df_grimage['beta'].tolist()).unsqueeze(0).float()
base_model.linear.bias.data = torch.tensor([0]).float()

model.base_model = base_model

Load reference values#

[9]:

reference_df = pd.read_csv('datMiniAnnotation3_Gold.csv', index_col=0)
model.reference_values = reference_df.loc[model.features[:-2]]['gold'].tolist() + [1, 65] # 65 yo F

Load preprocess and postprocess objects#

[10]:

model.preprocess_name = None
model.preprocess_dependencies = None

[11]:

model.postprocess_name = 'cox_to_years'
model.postprocess_dependencies = None

Check all clock parameters#

[12]:

pya.utils.print_model_details(model)


%==================================== Model Details ====================================%
Model Attributes:

training: True
metadata: {'approved_by_author': '⌛',
 'citation': 'Lu, Ake T., et al. "DNA methylation GrimAge version 2." Aging '
             '(Albany NY) 14.23 (2022): 9484.',
 'clock_name': 'grimage2',
 'data_type': 'methylation',
 'doi': 'https://doi.org/10.18632/aging.204434',
 'notes': None,
 'research_only': True,
 'species': 'Homo sapiens',
 'version': None,
 'year': 2022}
reference_values: [0.422480272528644, 0.935109546405548, 0.0162959729801047, 0.502691053893618, 0.910839576323153, 0.710155040209873, 0.479121329208521, 0.905888314944049, 0.279992670790348, 0.117900358329507, 0.940987438881091, 0.761621096809391, 0.0721244934513398, 0.0851830172952001, 0.222068390557704, 0.103705423432714, 0.91516014793103, 0.748331163695382, 0.903928589429489, 0.524090323888757, 0.894685558616447, 0.647988638853782, 0.0581747999131966, 0.830024180811995, 0.209808614636345, 0.324296328128978, 0.118979846374564, 0.545425926051344, 0.92324324492159, 0.328288208993484]... [Total elements: 1032]
preprocess_name: None
preprocess_dependencies: None
postprocess_name: 'cox_to_years'
postprocess_dependencies: None
features: ['cg00036119', 'cg00102512', 'cg00126959', 'cg00161556', 'cg00252095', 'cg00277397', 'cg00332048', 'cg00356999', 'cg00398048', 'cg00412842', 'cg00417288', 'cg00417823', 'cg00456299', 'cg00480331', 'cg00481951', 'cg00497251', 'cg00500789', 'cg00534468', 'cg00543335', 'cg00554421', 'cg00558975', 'cg00564555', 'cg00574958', 'cg00684178', 'cg00684824', 'cg00695391', 'cg00695799', 'cg00706683', 'cg00744433', 'cg00844308']... [Total elements: 1032]
base_model_features: None
features_PACKYRS: [1031, 799, 782, 584, 894, 609, 225, 268, 16, 907, 388, 202, 941, 665, 497, 405, 700, 61, 110, 392, 1001, 598, 200, 252, 297, 1, 287, 680, 27, 298]... [Tensor of shape torch.Size([173])]
features_ADM: [1031, 581, 823, 168, 152, 248, 649, 437, 922, 910, 594, 803, 449, 275, 163, 770, 790, 364, 908, 811, 474, 359, 420, 438, 215, 585, 327, 978, 133, 801]... [Tensor of shape torch.Size([187])]
features_B2M: [1031, 581, 866, 424, 1025, 764, 157, 712, 803, 977, 449, 635, 879, 787, 716, 810, 87, 648, 519, 48, 456, 768, 540, 888, 363, 35, 804, 434, 1015, 450]... [Tensor of shape torch.Size([92])]
features_CystatinC: [1031, 25, 660, 36, 225, 311, 59, 449, 982, 451, 89, 306, 475, 420, 914, 574, 358, 644, 916, 456, 14, 218, 868, 880, 432, 647, 1028, 931, 652, 98]... [Tensor of shape torch.Size([88])]
features_GDF15: [1031, 846, 885, 728, 974, 452, 449, 708, 544, 511, 539, 829, 729, 276, 831, 90, 362, 23, 1023, 186, 648, 286, 951, 962, 626, 189, 804, 532, 480, 67]... [Tensor of shape torch.Size([138])]
features_Leptin: [486, 581, 919, 775, 625, 444, 661, 213, 391, 603, 790, 908, 272, 334, 58, 420, 530, 786, 224, 381, 608, 91, 15, 1013, 683, 309, 1021, 455, 722, 549]... [Tensor of shape torch.Size([187])]
features_PAI1: [429, 330, 714, 421, 33, 636, 582, 12, 226, 558, 953, 509, 629, 766, 607, 824, 594, 774, 670, 789, 56, 792, 958, 571, 122, 991, 965, 191, 926, 970]... [Tensor of shape torch.Size([211])]
features_TIMP1: [1031, 764, 947, 883, 456, 912, 338, 434, 258, 476, 940, 739, 795, 473, 930, 956, 943, 534, 299, 702, 166, 800, 487, 326, 376, 514, 898, 936, 980, 423]... [Tensor of shape torch.Size([43])]
features_LogCRP: [57, 285, 147, 242, 47, 543, 809, 991, 101, 732, 494, 126, 115, 184, 359, 317, 860, 865, 172, 538, 887, 408, 985, 295, 556, 357, 838, 244, 506, 773]... [Tensor of shape torch.Size([132])]
features_A1C: [1031, 588, 341, 316, 47, 104, 685, 63, 474, 923, 777, 563, 906, 873, 361, 172, 808, 343, 862, 705, 152, 583, 909, 207, 985, 295, 575, 476, 23, 970]... [Tensor of shape torch.Size([87])]

%==================================== Model Details ====================================%
Model Structure:

PACKYRS: LinearModel(
  (linear): Linear(in_features=174, out_features=1, bias=True)
)
ADM: LinearModel(
  (linear): Linear(in_features=188, out_features=1, bias=True)
)
B2M: LinearModel(
  (linear): Linear(in_features=93, out_features=1, bias=True)
)
CystatinC: LinearModel(
  (linear): Linear(in_features=89, out_features=1, bias=True)
)
GDF15: LinearModel(
  (linear): Linear(in_features=139, out_features=1, bias=True)
)
Leptin: LinearModel(
  (linear): Linear(in_features=188, out_features=1, bias=True)
)
PAI1: LinearModel(
  (linear): Linear(in_features=212, out_features=1, bias=True)
)
TIMP1: LinearModel(
  (linear): Linear(in_features=44, out_features=1, bias=True)
)
LogCRP: LinearModel(
  (linear): Linear(in_features=133, out_features=1, bias=True)
)
A1C: LinearModel(
  (linear): Linear(in_features=88, out_features=1, bias=True)
)
base_model: LinearModel(
  (linear): Linear(in_features=12, out_features=1, bias=True)
)

%==================================== Model Details ====================================%
Model Parameters and Weights:

PACKYRS.linear.weight: [0.14214389026165009, 14.697949409484863, 0.4599894881248474, 0.3822956085205078, 7.98643684387207, 1.6803100109100342, 1.0967497825622559, 16.303823471069336, 2.4014580249786377, 0.6859070062637329, 1.6773189306259155, 21.501564025878906, -2.096100330352783, 2.2927305698394775, 0.12879624962806702, 0.5189002752304077, 9.517245292663574, 1.3636956214904785, 1.7754020690917969, 2.1244921684265137, 3.7083091735839844, 3.0460753440856934, 1.3274203538894653, -0.6062915921211243, -1.1171971559524536, -13.956497192382812, 0.36579036712646484, -0.6485168933868408, 4.881432056427002, -24.69486427307129]... [Tensor of shape torch.Size([1, 173])]
PACKYRS.linear.bias: tensor(-31.9970)
ADM.linear.weight: [0.9436950087547302, 4.995108127593994, 5.08618688583374, 28.64090347290039, 6.462732315063477, -118.2184066772461, -2.752854585647583, -55.56800079345703, -0.6833848357200623, 0.8265380263328552, -8.586676597595215, 9.290790557861328, 281.4186706542969, 9.880138397216797, -1.110060691833496, -0.036802105605602264, 202.86256408691406, -114.29457092285156, 248.89732360839844, 5.330321311950684, 4.495867729187012, -4.7390031814575195, 133.71437072753906, -2.2405805587768555, -3.3119983673095703, 19.081783294677734, 2.63143253326416, -24.076101303100586, -8.62603759765625, -32.408607482910156]... [Tensor of shape torch.Size([1, 187])]
ADM.linear.bias: tensor(290.1693)
B2M.linear.weight: [10486.9150390625, 316962.65625, 33927.10546875, -160612.375, 75457.171875, 87985.7421875, 292882.90625, -23280.169921875, 43791.1796875, 302011.96875, 1916187.0, -58500.59375, -126869.8828125, 1506.065185546875, 1417.4544677734375, 44895.46875, 267379.5625, -924930.5625, 69711.0390625, 102607.9921875, 49483.265625, 65359.765625, -13569.71875, -13531.2998046875, -84787.703125, -129131.7265625, 412413.875, -67296.7265625, 28426.35546875, 89744.1875]... [Tensor of shape torch.Size([1, 92])]
B2M.linear.bias: tensor(1412953.3750)
CystatinC.linear.weight: [2589.667724609375, -15088.66015625, 36553.1171875, -14194.40234375, 177517.65625, 2264.057861328125, -3639.993896484375, 146477.484375, 11819.2109375, 438.5729064941406, -35147.8515625, -146515.59375, 3482.841796875, 46536.5078125, -19400.65625, 5430.861328125, -2332.132080078125, 28774.947265625, 37130.91796875, 4642.5302734375, 2118.04541015625, 8312.26953125, 1070.0323486328125, 53286.4609375, 6551.3515625, 1233.9503173828125, -2420.194580078125, 3439.35546875, 13203.8330078125, 35212.96875]... [Tensor of shape torch.Size([1, 88])]
CystatinC.linear.bias: tensor(1091528.5000)
GDF15.linear.weight: [9.351357460021973, 84.36457824707031, 143.69606018066406, 81.37864685058594, 29.040103912353516, -112.49447631835938, 11.24372673034668, -118.38355255126953, -1980.7607421875, 12.090482711791992, 381.68292236328125, 20.09428596496582, 79.332275390625, 93.16657257080078, -42.04895782470703, -56.21128463745117, 50.33696746826172, -4.690526962280273, 23.865774154663086, 110.17974090576172, -504.0067138671875, 454.8924255371094, 3.939922571182251, 10.757635116577148, -21.75938606262207, 191.6622314453125, 342.773193359375, -183.74392700195312, 297.9110107421875, 50.03202438354492]... [Tensor of shape torch.Size([1, 138])]
GDF15.linear.bias: tensor(1975.7983)
Leptin.linear.weight: [399.8157043457031, 3861.580810546875, 4281.87353515625, 1714.1119384765625, -25588.5390625, 25643.771484375, -3710.89697265625, 1028.21484375, 4559.81591796875, -9729.9609375, 28351.38671875, 60980.60546875, 894.3289184570312, 2256.300537109375, -997.83447265625, 16759.64453125, 637.7293090820312, -14579.912109375, -7351.828125, 37.74680709838867, 5151.525390625, -38035.12890625, -3955.989990234375, -2736.428466796875, 154.31683349609375, -5049.8408203125, 4.860612869262695, -59299.390625, -594.4842529296875, 3549.596435546875]... [Tensor of shape torch.Size([1, 187])]
Leptin.linear.bias: tensor(7210.0625)
PAI1.linear.weight: [62.57840347290039, 321.195556640625, -476.72576904296875, 6221.58544921875, 7843.1796875, -313.1407775878906, -3855.91650390625, 3294.65234375, 752.2315673828125, -47.931236267089844, 301.0967712402344, -321.5203552246094, 24.402000427246094, -568.0665893554688, -3272.8876953125, 1760.7930908203125, -6259.56103515625, -10119.8154296875, 2037.0191650390625, 2472.403564453125, -3049.89794921875, 4225.28271484375, 982.7288208007812, -152.87660217285156, -356.9750061035156, -4542.0302734375, 433.330810546875, -169.24246215820312, -2095.550537109375, 311.5205078125]... [Tensor of shape torch.Size([1, 211])]
PAI1.linear.bias: tensor(-1129.6017)
TIMP1.linear.weight: [127.23798370361328, 576.6142578125, -161.49070739746094, -186.5166778564453, 571.6375732421875, 174.81607055664062, 23.66378402709961, -228.55433654785156, 58.980308532714844, 469.25677490234375, 723.093994140625, 1335.6502685546875, 542.5457153320312, 2160.827880859375, 922.79736328125, 7743.75146484375, -1151.7979736328125, -43.27967834472656, 407.7511901855469, -5735.69287109375, -11.83304500579834, -665.969970703125, 340.971923828125, 207.72994995117188, -32.84348678588867, -1965.6759033203125, 253.16822814941406, 23.78565788269043, 3192.898681640625, 67.02117156982422]... [Tensor of shape torch.Size([1, 43])]
TIMP1.linear.bias: tensor(15844.5957)
LogCRP.linear.weight: [-0.2381139099597931, 0.13791120052337646, 0.3978428244590759, 0.44597509503364563, 0.06502892822027206, -0.11037616431713104, 0.0026236912235617638, 0.13811077177524567, -1.9391815662384033, 0.02908332832157612, 0.28280875086784363, 0.9672318696975708, -0.09226538240909576, -0.20673252642154694, -0.8090327382087708, 3.308124303817749, 0.3115508258342743, 0.24958042800426483, 0.009494591504335403, -0.5394561886787415, 0.852174699306488, 0.03074250929057598, 0.02842751331627369, -1.275362491607666, 2.463428497314453, 0.08571851998567581, -0.8256807923316956, -1.2374017238616943, 0.29840514063835144, -0.22478485107421875]... [Tensor of shape torch.Size([1, 132])]
LogCRP.linear.bias: tensor(-0.2314)
A1C.linear.weight: [4.8407757276436314e-05, 0.2056836038827896, -0.13255050778388977, -0.008526227436959743, 0.05746829882264137, 0.012243995442986488, -0.09515052288770676, -0.006345819681882858, 0.016126316040754318, 0.0014027197612449527, 0.11271747201681137, -0.0036691571585834026, -0.012557895854115486, 0.08038703352212906, 0.02936549112200737, 0.0017015821067616343, 0.00732465973123908, 0.014937152154743671, -0.21280202269554138, 0.012396476231515408, -0.03226955980062485, 0.007510123774409294, 0.03052537515759468, 0.007964993827044964, 0.044394396245479584, -0.16780716180801392, -0.00614670105278492, 0.04923776164650917, -0.020067188888788223, 0.003006122075021267]... [Tensor of shape torch.Size([1, 87])]
A1C.linear.bias: tensor(1.6134)
base_model.linear.weight: tensor([[ 3.4967e-04,  2.7923e-07,  4.0842e-06,  1.3738e-04,  6.0893e-03,
          3.6692e-06, -2.0313e-05,  2.9409e-02,  4.0359e-01,  1.9027e+00,
          2.6764e-02, -1.4212e-01]])
base_model.linear.bias: tensor([0.])

%==================================== Model Details ====================================%

Basic test#

[13]:

torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float).double()
model.eval()
model.to(float)
pred = model(input)
pred

[13]:

tensor([[ 49.3385],
        [138.7238],
        [ 47.8732],
        [-62.3811],
        [112.4631],
        [-54.7387],
        [215.1543],
        [-53.0203],
        [ 72.2245],
        [-58.2163]], dtype=torch.float64, grad_fn=<AddBackward0>)

Save torch model#

[14]:

torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")

Clear directory#

[15]:

# Function to remove a folder and all its contents
def remove_folder(path):
    try:
        shutil.rmtree(path)
        print(f"Deleted folder: {path}")
    except Exception as e:
        print(f"Error deleting folder {path}: {e}")

# Get a list of all files and folders in the current directory
all_items = os.listdir('.')

# Loop through the items
for item in all_items:
    # Check if it's a file and does not end with .ipynb
    if os.path.isfile(item) and not item.endswith('.ipynb'):
        os.remove(item)
        print(f"Deleted file: {item}")
    # Check if it's a folder
    elif os.path.isdir(item):
        remove_folder(item)

Deleted file: grimage2_subcomponents.csv
Deleted file: datMiniAnnotation3_Gold.csv
Deleted file: grimage2.csv
Contents