fix(ml): fix test files using wrong foxhunt_ml:: crate name
Replaced foxhunt_ml:: with ml:: in 4 test files: - dqn_full_gradient_flow_integration_test.rs - dqn_gradient_flow_isolation_test.rs - tft_int8_forward_integration_test.rs - tft_int8_integration_test.rs Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -3,11 +3,11 @@
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// Created: 2025-11-21 (Test-Driven Development Campaign)
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use candle_core::{DType, Device, Tensor};
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use foxhunt_ml::dqn::config::DQNConfig;
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use foxhunt_ml::dqn::dqn::DistributionalDuelingConfig;
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use foxhunt_ml::dqn::replay_buffer_type::ReplayBufferType;
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use foxhunt_ml::error::MLError;
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use foxhunt_ml::trainers::dqn::{DQNTrainer, DQNTrainerConfig};
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use ml::dqn::config::DQNConfig;
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use ml::dqn::dqn::DistributionalDuelingConfig;
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use ml::dqn::replay_buffer_type::ReplayBufferType;
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use ml::error::MLError;
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use ml::trainers::dqn::{DQNTrainer, DQNTrainerConfig};
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use std::sync::Arc;
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/// Helper function to create minimal DQN config for testing
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@@ -4,8 +4,8 @@
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use candle_core::{DType, Device, Tensor, Var};
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use candle_nn::{linear, AdamW, Linear, Optimizer, VarBuilder, VarMap};
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use foxhunt_ml::dqn::dqn::{DistributionalDuelingConfig, DistributionalDuelingQNetwork};
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use foxhunt_ml::error::MLError;
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use ml::dqn::dqn::{DistributionalDuelingConfig, DistributionalDuelingQNetwork};
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use ml::error::MLError;
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/// Test 1: Network Forward Pass Gradient Flow
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/// Goal: Verify gradients flow through DistributionalDuelingQNetwork.forward()
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@@ -4,7 +4,7 @@
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use anyhow::Result;
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use candle_core::{DType, Device, Tensor};
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use foxhunt_ml::tft::{QuantizedTemporalFusionTransformer, TFTConfig};
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use ml::tft::{QuantizedTemporalFusionTransformer, TFTConfig};
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#[test]
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fn test_quantized_tft_forward_pass_integration() -> Result<()> {
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@@ -6,9 +6,9 @@
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//! 3. Checkpoint saving with metadata
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//! 4. Verify memory savings
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use foxhunt_ml::checkpoint::FileSystemStorage;
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use foxhunt_ml::tft::training::{TFTBatch, TFTDataLoader};
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use foxhunt_ml::trainers::tft::{TFTTrainer, TFTTrainerConfig};
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use ml::checkpoint::FileSystemStorage;
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use ml::tft::training::{TFTBatch, TFTDataLoader};
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use ml::trainers::tft::{TFTTrainer, TFTTrainerConfig};
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use ndarray::Array2;
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use std::path::PathBuf;
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use std::sync::Arc;
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