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Scale fix #30

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Scale fix #30

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fec7017
added scale_fix and grad_fix fixes in code
Haoranxia Jan 24, 2025
ad79f3d
removed grad_fix related code
Haoranxia Jan 28, 2025
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119 changes: 35 additions & 84 deletions example_basic_usage.ipynb
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12 changes: 9 additions & 3 deletions hyperbolicTSNE/cost_functions_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -109,7 +109,8 @@ def exact_tsne(cls):
"degrees_of_freedom": 1,
"skip_num_points": 0,
"num_threads": _openmp_effective_n_threads(),
"verbose": False
"verbose": False,
"grad_fix": False, # tells the cost function calculation to use the correct gradient
}
}

Expand All @@ -129,8 +130,13 @@ def bh_tsne(cls, angle=0.5):
"""
return {
"method": "barnes-hut",
"params": {"angle": angle, "degrees_of_freedom": 1, "num_threads": _openmp_effective_n_threads(),
"verbose": False}
"params": {
"angle": angle,
"degrees_of_freedom": 1,
"num_threads": _openmp_effective_n_threads(),
"verbose": False,
"grad_fix": False, # tells the cost function calculation to use the correct gradient
}
}

#########################
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5 changes: 3 additions & 2 deletions hyperbolicTSNE/solver_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,7 @@ def gradient_descent(
y0, cf, cf_params, *, start_it=0, n_iter=100, n_iter_check=np.inf, n_iter_without_progress=300,
threshold_cf=0., threshold_its=-1, threshold_check_size=-1.,
momentum=0.8, learning_rate=200.0, min_gain=0.01, vanilla=False, min_grad_norm=1e-7, error_tol=1e-9, size_tol=None,
verbose=0, rescale=None, n_iter_rescale=np.inf, gradient_mask=np.ones, grad_scale_fix=False,
verbose=0, rescale=None, n_iter_rescale=np.inf, gradient_mask=np.ones, grad_scale_fix=True,
logging_dict=None, logging_key=None,
):
"""Batch gradient descent with momentum and individual gains.
Expand Down Expand Up @@ -246,7 +246,8 @@ def gradient_descent(
error, grad = cf.obj_grad(y, **cf_params)

if isinstance(cf, HyperbolicKL):
# New Fix
# New Fix: Inverse metric tensor factor that multiplies the derivative of the cost function
# leading to a correct gradient update step
if grad_scale_fix:
grad = ((1. - np.linalg.norm(y.reshape(n_samples, 2), axis=1)
** 2) ** 2)[:, np.newaxis] * grad.reshape(n_samples, 2) / 4
Expand Down