text stringlengths 3 8.33k | repo stringclasses 52
values | path stringlengths 6 141 | language stringclasses 35
values | sha stringlengths 64 64 | chunk_index int32 0 273 | n_tokens int32 1 896 |
|---|---|---|---|---|---|---|
|D|/2 −log P(D | M)\n= minimising length of message needed to encode model + data (given\nthe model)\n\u0011 “Minimum Message Length (MML) Principle”\n31 / 55"
},
{
"id": "lecture-10-p032",
"sourceId": "lecture-10",
"lectureId": "lecture-10",
"page": 32,
"title": "32 / 55",
"... | mlpc-exam | lib/data/slide-index.json | JSON | 5a886d991cf525acfcb1ac7d7df9abcb7538fe778bdb60d21090a0afa5411d5a | 261 | 896 |
\n\u0011 Model A is better than model B if code(MA + EA) < code(MB + EB)\n35 / 55"
},
{
"id": "lecture-10-p036",
"sourceId": "lecture-10",
"lectureId": "lecture-10",
"page": 36,
"title": "▶View a cluster model as a kind of probability distribution model for which we",
"text":... | mlpc-exam | lib/data/slide-index.json | JSON | 85fae726d886a6bccad6f83bf54471e5de9ca41276fbe238f48b3f28ddc86cf7 | 262 | 896 |
"bic",
"the",
"circular",
"cluster",
"unsupervised",
"motivation",
"statistical",
"modelling"
],
"retrievalText": "Unsupervised Learning. . Motivation\nClustering\nStatistical Data Modelling\nBIC and the Minimum Message Length Principle\nApplying the ... | mlpc-exam | lib/data/slide-index.json | JSON | 7a162e8a5eedef673a4eb67148a857fb06f17c6a5ac85644043f6c487fb702ef | 263 | 896 |
",
"lectureId": "lecture-10",
"page": 39,
"title": "\u0011 Goal: Generative statistical model derived from the data",
"text": "Motivation\nClustering\nStatistical Data Modelling\nTowards Statistical Data Modelling\nMotivation:\n▶Clustering algorithms (e.g., k-means) only describe association of ... | mlpc-exam | lib/data/slide-index.json | JSON | 434b17931cf66873c0b330bee77827fb046557e85641f1b5ff9c54df7acf9092 | 264 | 896 |
probability of x under ith component distribution pi\n40 / 55"
},
{
"id": "lecture-10-p041",
"sourceId": "lecture-10",
"lectureId": "lecture-10",
"page": 41,
"title": "i=1",
"text": "Motivation\nClustering\nStatistical Data Modelling\nStatistical Data Modelling via Mixture Mo... | mlpc-exam | lib/data/slide-index.json | JSON | 08618cafcc76e7979ce63637fa2e16883b50c0d1066ab77361073744663ce19d | 265 | 896 |
"components",
"models",
"gmms",
"and",
"unsupervised",
"motivation"
],
"retrievalText": "Unsupervised Learning. sum of k Gaussian distributions (“components”):. Motivation\nClustering\nStatistical Data Modelling\nGaussian Mixture Models (GMMs) and the E-M Algorithm\nG... | mlpc-exam | lib/data/slide-index.json | JSON | 54d226a7531b2fc2793f83cd892036c9187124c6753912ddabbeaca4d6cf2708 | 266 | 896 |
the E-M Algorithm\nLearning GMMs from Data: How?\nIf we knew ...\n... which point ‘belongs to’ which component\n(i.e., which component generated each data point):\nCould easily fit model parameters by collecting all points belonging to each\ncomponent Ci and estimating wi, µi, Σi of the Gaussian Ci from these\n(see chap... | mlpc-exam | lib/data/slide-index.json | JSON | 1bf15e9efae16f36a3171d68ac85f810eec4a4e00b5933eb8632ebddb2e34268 | 267 | 896 |
i = P\nj qijxj/qi\nΣi = P\nj qij(xj −µi)(xj −µi)T /qi\nwi = qi\n45 / 55",
"keywords": [
"qij",
"compute",
"components",
"the",
"e-m",
"algorithm",
"parameters",
"probabilistic",
"assignment",
"instances",
"data",
"gmms... | mlpc-exam | lib/data/slide-index.json | JSON | cc9b91171f9f41049892030c662105aa77741cf01c91150baaa0c9b63f6ec55f | 268 | 896 |
,
"clustering",
"statistical",
"data",
"modelling",
"gaussian",
"mixture",
"models",
"and"
],
"retrievalText": "Unsupervised Learning. 47 / 55. Motivation\nClustering\nStatistical Data Modelling\nGaussian Mixture Models (GMMs) and the E-M Algor... | mlpc-exam | lib/data/slide-index.json | JSON | d343d7958561de7aad525ed5a6fdf477aaf28771f1c11ce0d28bee8a3ca0a886 | 269 | 896 |
"page": 51,
"title": "0",
"text": "Motivation\nClustering\nStatistical Data Modelling\nGaussian Mixture Models (GMMs) and the E-M Algorithm\nProperties of the E-M Algorithm\nInterpretation of the E-M Steps:\n▶E (Expectation) Step: Can be interpreted as computing the expected value\nof the hidden indicator v... | mlpc-exam | lib/data/slide-index.json | JSON | 7d236cf232c65e05aca1719e3018cdaabcb7d5424ac66c1942892c31bc81f8c2 | 270 | 896 |
cluster Ci)\nGMMs as Density Estimation:\n▶By construction, a GMM is a proper density function p(x) over feature space\n⇒can use it as a density estimator for p(x | ωi) for classification via Bayes’\nrule!\n▶GMM can generate (sample) new points that obey the same distribution\n▶Training algorithm estimates model paramet... | mlpc-exam | lib/data/slide-index.json | JSON | 0d2f43491fd27167b3304483e7dfe37ac8c12f2efe5717b0a5d3815d164a31f8 | 271 | 896 |
terminate with k∗= 1 and select a degenerate 1-cluster solution\n▶it would select k∗= |D| and select a degenerate 1-example-per-cluster solution\n▶it would give the same result for all k and thus be useless for selecting among\ndifferent k\n▶it can only be computed for those k that are divisors of N = |D|\n▶it cannot b... | mlpc-exam | lib/data/slide-index.json | JSON | fe8ab497ec4889f2bb2988de064d4c383ca18060b4f5cde906ee98ba003bfc40 | 272 | 896 |
objects, we need to estimate\n▶1/10 the number of parameters\n▶the same number of parameters\n▶10 times as many parameters\n▶102 times as many parameters\n▶102 times as many plus 990 parameters.\n55 / 55",
"keywords": [
"the",
"cluster",
"parameters",
"many",
"gmm",
... | mlpc-exam | lib/data/slide-index.json | JSON | 09f9b9964faf9183ebdd9db05fefdf5320f327abd62d3c6462909c499c7104e6 | 273 | 451 |
# -*- coding: utf-8 -*-
"""Build the MLPC static study dataset from local PDFs.
The question records are curated because Moodle PDF exports interleave useful
exam content with attempt-review noise. Slide chunks are extracted directly so
questions can be linked to lecture material and vectorized later.
"""
from __futu... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 3e06e4ab8b7d2735aed70e5c7dd4eef72662bd079f39e691f5fd4f87c05b5666 | 0 | 896 |
.37.39_3.jpeg", "closed-book-2026-group-b-page-05.jpeg", 5, "2026 closed-book exam image, Group B page 5", None),
("img-closed-book-2026-p06", "WhatsApp_Image_2026-06-15_at_13.37.39.jpeg", "closed-book-2026-group-b-page-06.jpeg", 6, "2026 closed-book exam image, Group B page 6", None),
("img-closed-book-2026-p0... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 95fb29a76a6b037987df1209ba383e7ded22d3b7154bbd55d6d03f7c37370a36 | 1 | 896 |
"lectureIds": ["lecture-05"],
"description": "ID3, entropy, information gain, symbolic vs numeric splits, pruning, and tree depth.",
},
{
"id": "common-classifiers",
"title": "Common Classifiers",
"lectureIds": ["lecture-05"],
"description": "Naive Bayes, SVMs, k-NN, deci... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 3d2bc0a6e8e6aa93fb40f45ae88c8fd755fdbd58c890e61a0c547d78742af08f | 2 | 896 |
-Za-z0-9_-]{2,}", text.lower())
return [word for word in words if word not in STOPWORDS]
def keywords(text: str, limit: int = 12) -> list[str]:
counts = Counter(tokens(text))
return [word for word, _ in counts.most_common(limit)]
def extract_slide_chunks(now: str) -> dict[str, Any]:
chunks: list[dic... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | b2a411fbe926b67a5a0542649c0ac74baef98eadfbefd17918d254739f678283 | 3 | 896 |
in TOPIC_BY_ID[topic_id]["lectureIds"]:
if lecture_id not in seen:
seen.append(lecture_id)
return seen
def base_question(
*,
qid: str,
origin: str,
source_id: str,
page: int | None,
question_number: str | None,
topic_ids: list[str],
difficulty: str,
... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | fd8dd1cad4b52359398d9afcb20e413d5cbe5fc30ab0aed4ba1e6646092130e3 | 4 | 896 |
def add_multi(
qid: str,
source_id: str,
page: int,
qn: str,
topic_ids: list[str],
stem: str,
choices: list[str],
correct_indices: list[int],
explanation: str,
tags: list[str],
*,
origin: str = "real_exam",
answer_source: str = "explicit_pdf",
confidence: float = ... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | a0331ff38bd3944354e466cc2394d015e7167731e8a803ade7aa4f974aaa4671 | 5 | 896 |
-bayes-fish")
add_tf("real-retake1-q002", "exam-retake-1", 1, "2", ["bayesian-classification"], f"{bayes_fish}\nFor any fish x in D, p(x) is a vector with 100 values.", False, "The feature vector x has 100 components, but p(x) is one scalar density or probability value.", ["bayes-rule", "feature-space"], group_id="... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 1860924f975c740c4efcf9e400b1d937d6594c438261e55ea7697951b00992fd | 6 | 896 |
real-retake2-q009", "exam-retake-2", 1, "9", ["density-estimation"], f"{density_context}\nThe y values in the lower plot cannot be probabilities, because they are negative.", True, "Negative y-values cannot be probability values; the lower plot is not a probability/likelihood curve.", ["probability", "likelihood"], gro... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | a04c12f1ff4f58bd9af6935b865dd15c8d85b60af73ce6d53faeb574524e7f0e | 7 | 896 |
False, "With k = N, each point is classified by the global majority vote, so minority-class training examples are misclassified.", ["knn", "resubstitution"], group_id="retake3-knn-k-equals-n")
add_tf("real-retake3-q017", "exam-retake-3", 2, "17", ["nearest-neighbour"], f"{knn_context}\nThe resubstitution estimate o... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 3ba8c0bda7d2242f0aa0cec6832ed1da39f7d8b70f711c31261388c84c18f6bf | 8 | 896 |
-q024", "exam-retake-5", 1, "24", ["common-classifiers"], f"{visual_dataset_context}\nAn SVM with a quadratic kernel can achieve an error of zero on this dataset.", True, "The quadratic feature mapping can make the dataset linearly separable in the transformed space.", ["svm", "kernel"], group_id="retake5-various-class... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 8e25f342d1c1413cba4d9358286cb4f819a431475d9615ef08fbf56661fa3857 | 9 | 896 |
, 3, "30", ["neural-networks", "classifier-evaluation"], f"{nn_threshold_context}\nIncreasing τ cannot increase the recall of the model on class 0 on the training set.", False, "Raising the threshold makes class 0 easier to predict, so recall for class 0 can increase.", ["threshold", "recall"], group_id="retake5-nn-thr... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 7f0d2e67c7463026eeecd04ef20bc3b3a097355670a416edadca270d034864af | 10 | 896 |
"5 x 2", "In the lecture convention used by this exam, B has one row per observation symbol and one column per hidden state.", ["hmm", "observation-matrix"], answer_source="inferred_from_slides", confidence=0.88, group_id="retake6-hmm-dimensions")
add_numeric("real-retake6-q038", "exam-retake-6", 1, "38", ["markov-... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 9f35ac00d863ad4d2c5f4e6c6dfd2223a416efb49582b64146c5ef466687190d | 11 | 896 |
=0.94)
add_short("real-ss20-q002", source, 2, "2", ["bayesian-classification"], "What will happen if the prior P(Ω) is very uneven, with a high value for one class and a low value for another?", "The classifier is biased toward the high-prior class; if the prior is strong enough, it can dominate the likelihood.", "... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 007cce12d93b9fcabc53b5f6ba481c428de1b086bec32441f8aa24121635e427 | 12 | 896 |
distance is scale-sensitive, so one feature can dominate after rescaling.", ["knn", "normalization"])
add_numeric("real-ss20-q011", source, 4, "11", ["decision-trees"], "For the one-dimensional training sequence + + - + + + - + - - -, what is the minimum depth of a binary-split decision tree on X that has zero trai... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 83e05e0f0182121c48d6fd75d301e3137d54aaca8d3f6596cf64ac40ceda6683 | 13 | 896 |
a dataset, A must have lower precision on ω1 than B.", False, "Recall and precision trade off often, but no strict implication holds for arbitrary classifiers.", ["precision", "recall"])
add_tf("real-ss20-q020", source, 7, "20", ["classifier-evaluation"], "No line segment of the ROC convex hull can be strictly hori... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 51ffce2170bf8714f7a0cac43af9b998ed29d41a3c2d8875865d9e35bf23ef7a | 14 | 896 |
])
add_single("real-ss20-q029", source, 10, "30", ["neural-networks"], "When you add an additional hidden layer to a feed-forward network, how does that affect the variance of the network model?", ["it increases", "it decreases", "it is unchanged", "it becomes zero"], 0, "A more flexible model is more sensitive to ... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 004dd12736761ed920ed8b60a93bf2f17c24bf882b60f370aff9f2ac2593d74b | 15 | 896 |
; "
"S3 predict ω1 iff P(ω1|x) > P(ω2|x); "
"S4 predict ω1 iff P(ω1) > 0.5; "
"S5 predict ω1 iff p(x|ω1) > p(x); "
"S6 predict ω1 iff P(ω1|x) > P(ω1)."
)
options = ["S1", "S2", "S3", "S4", "S5", "S6"]
add_multi("real-ss22-q001", source, 1, "1", ["bayesian-classification"], f"... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 174d10f71f39eb1e9d3a1d8c12575dcec09c46ee5a7e11ac2c61211778d80773 | 16 | 896 |
ss22-q009", source, 4, "9", ["bayesian-classification"], "If P(ω1|x) > P(ω1), then P(ω2|x) < P(ω2) in a two-class problem.", True, "The two posterior probabilities and the two priors each sum to one, so an increase for one class implies a decrease for the other.", ["posterior", "two-class"], group_id="ss22-bayes-condit... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | a2e0f04f8c4ed25a529547e7b2002fad6f1b0af1ebc4de377b001962fa4dfb61 | 17 | 896 |
x=1.", ["precision", "recall"], group_id="ss22-rp-space")
add_single("real-ss22-q017", source, 7, "17", ["classifier-evaluation"], "In recall-precision space for class p, which point corresponds to an all-n classifier?", rp_choices, 5, "No positive predictions are made, so precision is undefined.", ["precision", "r... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 9858fa84e867fb9cce1e12c5360cd3c0af569f247ee3306418025585fb741e86 | 18 | 896 |
the classes perfectly.", ["tree-depth"], group_id="ss22-fish-dataset")
add_tf("real-ss22-q025", source, 11, "25", ["decision-trees"], f"{fish_context}\nOn this dataset, ID3 is guaranteed to find the simplest possible tree.", True, "The feature with perfect information gain gives a depth-1 tree.", ["id3", "informati... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 8efe5b705cc95c73e9653aee415ca84bfc63b85eadd7b99c754e19b3db24f97e | 19 | 896 |
class label is determined by Length, so a linear boundary separates x1=0 from x1=1.", ["linear-separability"], group_id="ss22-fish-linear")
add_numeric("real-ss22-q033", source, 13, "33", ["neural-networks"], f"{fish_context}\nHow many units are needed to express a classifier without hidden layer that predicts P(ba... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 942ce519b88485a11b3bb62bbda1605b177cd79058fb91637406d67caf2dfdc4 | 20 | 896 |
44", ["neural-networks"], "In gradient descent, a gradient of zero will result in unchanged weights.", True, "The weight update is proportional to the negative gradient, so zero gradient gives no update.", ["gradient-descent"])
add_tf("real-ss22-q045", source, 18, "45", ["neural-networks"], "A large learning rate m... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 6c935d2371b7daeeadb507d2c3e3da81176352d2b2df760a979b7505889db1f6 | 21 | 896 |
"real-ss22-q056", source, 22, "56", ["unsupervised-learning"], "For k-means, what quality does Mean Quantisation Error (MQE) implicitly try to encode?", ["average size of cluster centers", "compactness of clusters", "average distance between cluster centers", "equal distribution among clusters", "number of clusters"], ... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | db19e86759a0149bb07187834302238aaef5a62056c966e71fbc0c8a7bfc3034 | 22 | 896 |
-matrix"])
add_tf("real-ss22-q065", source, 26, "65", ["markov-models-hmm"], "The HMM state transition matrix is always square.", True, "It maps states to states, so it has one row and one column per state.", ["hmm", "transition-matrix"])
add_tf("real-ss22-q066", source, 26, "66", ["markov-models-hmm"], "The Fo... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 81db4eb5e069f2a20922f1b1786f714458780c10f6e6be73a2617a8ae4e3fb35 | 23 | 896 |
.", ["bayes-error", "risk"])
add_tf("real-2023-q005", source, 2, "5", ["bayesian-classification"], f"{cost_context}\nThe expected risk defines decision boundaries only in a two-dimensional feature space.", False, "Risk-based decision boundaries exist in any feature-space dimension.", ["decision-boundary", "risk"])
... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 7408e9c8b24a662f20761e20d66d074c1300db7cf7f9e1fbd68251d9a18c7ae0 | 24 | 896 |
C,D) = 1.0.", False, "Recall and precision are independent measures and do not generally sum to one.", ["precision", "recall"], answer_source="explicit_pdf")
add_tf("real-2023-q012", source, 5, "12", ["classifier-evaluation"], "If two classifiers have the same TPRs but different FPRs, they cannot have the same accu... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 6334c491576dbc996a366b21d66603be0e52ce1d6e4fdf7036f68c9e5bf3ec8b | 25 | 896 |
id3s_context}\nThis is a high-bias learner.", True, "Restricting trees to one split severely limits expressiveness.", ["bias", "id3"], answer_source="explicit_pdf")
add_tf("real-2023-q020", source, 8, "20", ["common-classifiers", "decision-trees"], f"{id3s_context}\nCombining different such trees via voting can imp... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | d3f7548e1942aa10603f9999bfa14b0c34fce52ff0691bf7976222c6010c9ff9 | 26 | 896 |
number of hidden layers.", ["cross-entropy"], answer_source="explicit_pdf")
add_tf("real-2023-q028", source, 11, "28", ["neural-networks"], f"{ce_context}\nIf the class distribution is 50:50, binary cross-entropy encourages predictions close to 0.5.", False, "Cross-entropy encourages confident correct predictions f... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | b5699f91aa269229aaf1122fd37f4c2e4d38e2e001dd202e8343f150ab23104b | 27 | 896 |
1/3]."
add_tf("real-2023-q036", source, 14, "36", ["markov-models-hmm"], f"{markov_context}\nIf some a_ij is changed to zero, then a_ji must also be zero for a valid Markov process.", False, "Transition probabilities need not be symmetric.", ["markov-chain", "transition-matrix"], answer_source="inferred_from_slides... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 1ff1dabaf9e01bb9162c20c64be9b64980fdc898663439e078bdbdb1aab8958a | 28 | 896 |
)", "P(S|M_A)", "P(B)"], [0, 1, 3, 4], "MAP compares P(S|M_A)P(A) with P(S|M_B)P(B).", ["map", "markov-model", "likelihood"], answer_source="inferred_from_slides", confidence=0.94)
add_tf("real-2023-q044", source, 16, "44", ["markov-models-hmm"], "If S2 is produced by concatenating two copies of S, the likelihoods ... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 3de4ee3167e666cae1e663c903b0e0569af9978b1475317181bc25574ae7c52b | 29 | 896 |
likelihood of data under parameters; posterior maximization would be MAP estimation.", ["mle", "likelihood"], "definition"),
("gen-density-002", ["density-estimation"], "A likelihood value for continuous data can be greater than 1.", True, "Continuous probability densities can exceed 1; probabilities over regio... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 28c5d47f487a7c2c75083ff216f1683ce5afb875b5863735e40dcabb7e4c132e | 30 | 896 |
", ["bagging", "ensemble"], "definition"),
("gen-improve-001", ["learning-improvement"], "Feature construction can make a classifier succeed even when the original feature space is not linearly separable.", True, "A transformed feature space can expose a simpler decision boundary.", ["feature-construction", "li... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | a7d423de9d329991e50691766375c35e64fd138d0d3aa504807ab282d35cb6bf | 31 | 896 |
centers.", ["k-means", "mqe"], "definition"),
("gen-unsup-002", ["unsupervised-learning"], "Increasing k in k-means always improves the BIC score.", False, "BIC balances fit with a complexity penalty, so more clusters are not always preferred.", ["bic", "k-means"], "conceptual"),
("gen-unsup-003", ["uns... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 6b0676c1558071bbdc6fec521a478bada329cb61eb41bc845194a57a368e496b | 32 | 896 |
bic", "model-selection"],
origin="generated_from_slides",
answer_source="generated",
)
def attach_retrieval_text_and_slide_refs(slide_index: dict[str, Any]) -> None:
chunks = slide_index["chunks"]
by_lecture: dict[str, list[dict[str, Any]]] = {}
for chunk in chunks:
by_lecture.... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 3361eb5394d2205a5cb490f1d275fb742bc7d7e2d64247b6d9cf36317329b4d8 | 33 | 896 |
,
tags: list[str],
*,
difficulty: str = "medium",
cognitive_level: str = "conceptual",
confidence: float = 0.96,
group_id: str | None = None,
) -> None:
add_single(
qid,
f"img-closed-book-2026-p{page:02d}",
page,
qn,
topic_ids,
stem,
ch... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 0a7e42f8301379269999ab73068a54e4c8002ada30bc6236c0a8945f01394425 | 34 | 896 |
class."
)
percent_choices = ["0%", "5%", "10%", "50%", "80%", "90%", "95%", "100%", "other"]
img_single("img-q001", 2, "1", ["classifier-evaluation"], f"{metric_context}\nWhat is the expected accuracy that T2 will achieve on the population of A?", percent_choices, 5, "T2 always predicts false, so it is corr... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 5f476679be75d018d80cf2f465959707a67ba5a23a319ded4b65a42db8300de2 | 35 | 896 |
.", ["cost-matrix", "risk"], cognitive_level="calculation", group_id="img-cost-decisions")
img_single("img-q008", 3, "8", ["bayesian-classification", "classifier-evaluation"], f"{cost_context}\nλ = [[0, 100], [100, 0]]", test_choices, 1, "Both error types are equally expensive, so the lowest expected error rate sti... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 5bacced81f67e489630875466fee35154838653408b6439c02e545ba5018d2eb | 36 | 896 |
", ["bayesian-classification"], "Not normalising by p(x) in a Bayesian MAP classifier tends to reduce its ability to overfit data.", False, "Dropping p(x) does not change MAP predictions because p(x) is constant across classes for a fixed x.", ["map", "evidence"], group_id="img-overfitting")
img_tf("img-q018", 5, "... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | d1b70cc2c16db668309a3d605e8646755d23caf0dab9d621ebad35616445e83b | 37 | 896 |
, "Gradient-based learning needs derivatives to compute weight updates.", ["loss", "differentiability"], group_id="img-ann")
img_single("img-q026", 6, "26", ["neural-networks"], "Binary cross-entropy is ...", ["a measure of difference between two binary class distributions", "a measure of influence between two bina... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 33d0dbf93c4e20e66b0e67b28048b3e7a4f0e853699730a21be0de0fe54235bf | 38 | 896 |
-q032", 7, "32", ["neural-networks"], "In early stopping, we stop training an ANN ...", ["when the training error reaches zero", "when the validation error reaches zero", "when the training error goes above the validation error", "when the validation error starts going up", "when the validation error reaches the traini... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | aae2322d65dd7e39a6fbc650edd6454a4d1c764cf7e5f2b3452a75dfe28f5bcb | 39 | 896 |
, "In an ID3 tree, no leaf can contain training examples of different classes", "If two training examples have the same feature values but different class labels, no decision tree can be 100% consistent with the training data", "There can be no dataset for which a consistent decision tree consists only of a root node",... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 470ddfef21fc75a879f3f4af93c30219b370e0930a25ba984db0faa026baad67 | 40 | 896 |
"kN + d parameters"], 0, "Each component needs a mean vector, covariance parameters, and a mixture weight.", ["gmm", "parameters"], cognitive_level="calculation", group_id="img-unsupervised")
img_single("img-q043", 10, "43", ["unsupervised-learning"], "In clustering, the Bayes Information Criterion (BIC) is used ..... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 34eb0813189919e6495b0f737828370de457e01e0aab3fced9491e8926c1da62 | 41 | 896 |
"k(d+1) parameters", "kN + d parameters", "kd^2 parameters", "kd parameters"], 6, "A k-means model stores k cluster centers, each with d coordinates.", ["k-means", "parameters"], cognitive_level="calculation", group_id="img-unsupervised")
def build_legacy_approved_questions() -> None:
legacy_tf("legacy-q001", "ex... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | ec10637f05c5af16a08972665f8451fbbf9ce15aae7ffdcfcfd3c09a968ed996 | 42 | 896 |
normalization"])
legacy_single("legacy-q011", "exam-ss20", 10, "29", ["neural-networks"], "Adding an additional hidden layer to a feed-forward network generally affects model bias how?", ["it increases", "it decreases", "it is unchanged", "it becomes undefined"], 1, "More expressive models generally have lower bias... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 9875e48fff97433054b23e09ce2e94900e2444bcae9078534c812423f86ed34a | 43 | 896 |
(θ|D)", "p(D|θ)", "P(ω|x)", "the prior P(θ) only"], 1, "MLE maximizes the likelihood of the observed data under the parameter.", ["mle"])
gen_tf("gen-closed-q007", ["density-estimation"], "For continuous variables, a probability density value can be larger than 1.", True, "Densities can exceed one; integrals over r... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 215f775802c09b1aeed1939f0de709b350b229ed14d71e036537ca1d37c2b0f0 | 44 | 896 |
])
gen_single("gen-closed-q018", ["nearest-neighbour"], "Why can feature normalization matter for k-NN?", ["It changes class priors", "It changes Euclidean distances", "It changes the number of examples", "It makes k irrelevant"], 1, "k-NN bases predictions on distances.", ["knn", "normalization"])
gen_tf("gen-... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 912452fb4cb87c2f477e6bd87c9c101f321bfa7666c5e231741f3dcde351160a | 45 | 896 |
, "The negative gradient is the local steepest descent direction.", ["gradient-descent"])
gen_tf("gen-closed-q029", ["neural-networks"], "A learning rate that is too large can overshoot good parameter values.", True, "Large updates can bounce across or diverge from minima.", ["learning-rate"])
gen_single("gen-c... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | e52c28fbbf9fbacbee9c8d3120fbeec7d29abd114eb1ef8dcc10ec79e5b106d7 | 46 | 896 |
q040", ["markov-models-hmm"], "The Forward algorithm is used to compute ...", ["the probability of an observation sequence", "the number of clusters", "the information gain of a feature", "the SVM margin"], 0, "It marginalizes over hidden state paths.", ["forward-algorithm"])
def main() -> None:
DATA_DIR.mkdir(pa... | mlpc-exam | scripts/build-mlpc-dataset.py | Python | 1b6cb31dc3e585f34bbd4becc1cb3240dab0d81a143af240cf1735c24fc996cb | 47 | 596 |
# Python
__pycache__/
*.py[cod]
*.pyo
.pytest_cache/
pytest_tmp/
.mypy_cache/
.ruff_cache/
.coverage
htmlcov/
# Virtual environments and local config
.venv/
venv/
env/
.env
.env.*
# Course dataset and raw audio
data/
resources/*.zip
resources_codex/
*.wav
*.flac
*.mp3
# Large generated artifacts
results/*.npz
result... | mlpc_task_4 | .gitignore | Git Ignore | 9bc2f8ffc81cc0b9b188bba0c6e51556987d199b6963bd72da249a6f730b82d7 | 0 | 207 |
[tool.ruff]
line-length = 100
target-version = "py311"
[tool.ruff.lint]
select = ["E", "F", "I", "UP", "B"]
ignore = ["E501"]
[tool.pytest.ini_options]
testpaths = ["tests"]
filterwarnings = [
"ignore:Stochastic Optimizer:sklearn.exceptions.ConvergenceWarning",
]
| mlpc_task_4 | pyproject.toml | TOML | c91bdbb96080c4057d4d5799fbcb19570570cb0c045be97f6c94565b9a2df4c0 | 0 | 85 |
# MLPC 2026 Task 4: Data Classification
This repository contains the code, report, slides, and tracked result summaries for
the MLPC 2026 Task 4 sound event classification assignment.
## Contents
- `src/` - data preparation, splits, preprocessing, training, evaluation, and report figures
- `tests/` - unit tests for ... | mlpc_task_4 | README.md | Markdown | 23fb6bbfb6fd59099ed980a089bd7348104fc1e5cfcab0ff3fe6f2ed2a08be9b | 0 | 424 |
numpy>=1.26
pandas>=2.1
scikit-learn>=1.4
matplotlib>=3.8
seaborn>=0.13
tqdm>=4.66
mlx>=0.18; sys_platform == "darwin" and platform_machine == "arm64"
librosa>=0.10
soundfile>=0.12
pyarrow>=15
joblib>=1.3
pytest>=8.0
| mlpc_task_4 | requirements.txt | Text | 7eca92e1d6acf09d6a3fb26b1474f7c7bd70f4e0ab3fea1a5e4076ce78a272a2 | 0 | 64 |
\documentclass{article}
\usepackage[final]{neurips_2023}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{hyperref}
\usepackage{url}
\usepackage{booktabs}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{nicefrac}
\usepackage{microtype}
\usepackage{xcolor}
\usepackage{graphicx}
\usepackage{subca... | mlpc_task_4 | report/main.tex | TeX | 2d4030696e7b783c29df99a727b8c201b5b00ffe10a557646f23c8a22d693c24 | 0 | 896 |
The split is collector-disjoint, not segment- or file-random. We use grouped random splitting with 70\%/15\%/15\% target proportions, yielding 116{,}950 train segments from 2{,}553 files and 285 collectors, 25{,}733 validation segments from 548 files and 61 collectors, and 25{,}556 test segments from 555 files and 62 c... | mlpc_task_4 | report/main.tex | TeX | 021a70ce143a042a98579d952e6dd52ed2688f034123aaf8511c459f8c031910 | 1 | 896 |
^{-4}$ and positive weights clipped at 10. Stronger class weighting improved rare-class recall but lowered ranking quality on common classes; no weighting improved micro AP but hurt macro AP.
% NOTE: Figure 2 (validation-AP settings bar charts, figures/hyperparameter_summary.pdf) was
% removed to meet the 3-page limit;... | mlpc_task_4 | report/main.tex | TeX | 6d847ca1c2485a8d0a2940128bb98bf85b443497457dcf83ef88a1db78e3ba7e | 2 | 896 |
{running\_water} (0.925 AP), \texttt{keyboard\_typing} (0.861), \texttt{vacuum\_cleaner} (0.847), and \texttt{microwave} (0.793). These produce long, repeated evidence across adjacent segments, so the context window and AP-based objective work well. The weakest classes are \texttt{light\_switch} (0.108), \texttt{wardro... | mlpc_task_4 | report/main.tex | TeX | 74f9a229f413a26faa65245ad4682197592f4e96f7934a89df305a54f7a52ee8 | 3 | 378 |
{
"test": {
"macro_ap": 0.06358323991298676,
"micro_ap": 0.12071314403081619,
"per_class_ap": {
"bell_ringing": 0.018077947199344635,
"coffee_machine": 0.03588198497891426,
"cutlery_dishes": 0.07446391880512238,
"door_open_close": 0.05137736722826958,
"footsteps": 0.155697286... | mlpc_task_4 | results/baseline.json | JSON | 35273f27b83ec83f36be4d0133573ddd27fe733195f0a94b7160ca9541413015 | 0 | 613 |
# Results Log
## Dataset Cache
- Files processed: 3656
- Total segments: 168239
- Feature dimensionality: 960
- Feature keys: bandwidth_max, bandwidth_mean, bandwidth_min, bandwidth_std, centroid_max, centroid_mean, centroid_min, centroid_std, contrast_max, contrast_mean, contrast_min, contrast_std, energy_max, energ... | mlpc_task_4 | results/log.md | Markdown | dabfad3fed53baca6655504cbeab4118811fb6f5c1711a471b1227f98b305711 | 0 | 316 |
{
"macro_ap": 0.617816150188446,
"micro_ap": 0.7172471440304844,
"members": [
"C:\\Development\\Private\\mlpc_task_4\\results\\mlp_refine_candidates\\mlp_01_512-256_d0.45_lr0.001_s42.pt",
"C:\\Development\\Private\\mlpc_task_4\\results\\mlp_refine_candidates\\mlp_02_1024-512_d0.35_lr0.001_s42.pt",
"C:... | mlpc_task_4 | results/mlp_ensemble_manifest.json | JSON | 8ce2c2c15deaa207a867841f5a04f0f50f22035b73935cc1f7a70a0e187cbf73 | 0 | 291 |
% !TeX program = xelatex
% !TeX encoding = UTF-8
% !TeX spellcheck = en_US
%%
%% MLPC 2026 Task 4 -- Case Study and Reflection
%% Restyled with the official JKU LaTeX Beamer theme
%% (https://github.com/michaelroland/jku-templates-presentation-latex)
%%
%% BUILD: compile with XeLaTeX (or LuaLaTeX) so the JKU corporat... | mlpc_task_4 | slides/slides.tex | TeX | 9971d1a10e759a0fe149d77d6eb8e3f317392a7a159fb409e15db9e2dcb5e224 | 0 | 896 |
------------------------------------------
\begin{frame}{Qualitative Case Studies}
\begin{columns}[T]
\begin{column}{0.49\textwidth}
\textbf{Success: file 000727}
\begin{itemize}
\item Kitchen (microwave, water, dishes); static phone
\item File-level F1: \textbf{0.978}
\item Su... | mlpc_task_4 | slides/slides.tex | TeX | cfe40bf23ff3cf8b6104300b257fc326aacee0c96e12e7f514111ce261d4aadc | 1 | 896 |
{Mechanical confusions:}
\texttt{door}, \texttt{window}, and \texttt{wardrobe\_drawer} share impact, hinge, and handling sounds; the feature representation separates them only weakly.
\vspace{0.35em}
\item \textbf{Rare-class instability:}
Few positive examples make AP sensitive to label noise and misse... | mlpc_task_4 | slides/slides.tex | TeX | ed9f8cb7aa6801d1254a733392ab6f9866c2fd5c9947ea76907256f2f04741c1 | 2 | 137 |
"""MLPC 2026 Task 4 classification pipeline."""
| mlpc_task_4 | src/__init__.py | Python | 80ac7cd492d41224ef634f7f31967ee7d6c0d76156b5ee8e253f02e07776f6f6 | 0 | 13 |
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import numpy as np
from . import config
from .metrics import macro_ap, micro_ap, per_class_ap, per_class_f1_at_optimal
from .splits import load_dataset_cache
def class_prior_baseline_scores(y_train: np.ndarray, n_rows: i... | mlpc_task_4 | src/baseline.py | Python | 418d03a04dd1231a48fd069a9fd8f67b0e245d2482c16309c7d8c24e27a3d2ff | 0 | 583 |
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
DATA_DIR = ROOT / "data"
FEATURES_DIR = DATA_DIR / "audio_features"
METADATA_CSV = DATA_DIR / "metadata.csv"
ANNOTATIONS_CSV = DATA_DIR / "annotations.csv"
RESULTS_DIR = ROOT / "results"
FIG_DIR = RESULTS_DIR / "figures"
DATASET_CACHE = RESULTS_DIR /... | mlpc_task_4 | src/config.py | Python | 035e853f9f031cb613fe902537bc41ad8c06a0a9735b231019fe3c685f28400c | 0 | 413 |
from __future__ import annotations
from collections.abc import Mapping
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
from . import config
NON_FEATURE_KEYS = {
"annotations",
"annotation",
"class_names",
"annotator_ids",
"annotators",
"start_time",
... | mlpc_task_4 | src/data.py | Python | 52e47f5613e5a59f40c7dd3c3f3938f7b2401cbd6c1e53c2cdaf5ef37db1ff5e | 0 | 896 |
: tuple[str, ...], segment_count: int
) -> np.ndarray:
for key in keys:
if key in npz_dict:
arr = np.asarray(npz_dict[key], dtype=np.float32)
if arr.shape[0] != segment_count:
raise ValueError(
f"{key!r} has segment count {arr.shape[0]}, expected {... | mlpc_task_4 | src/data.py | Python | 057fdc9acfa27903081d9c4217e779c3bb7a129342279d9712f5352990182d19 | 1 | 896 |
ValueError(f"Class names in {feature_file} do not match config.CLASS_NAMES")
if "annotations" not in npz_dict:
raise ValueError(f"{feature_file} does not contain an 'annotations' array")
features, feature_keys = concat_features(npz_dict)
labels = aggregate_labels(np.asarray(npz_dict... | mlpc_task_4 | src/data.py | Python | b21863b1961f0a1c3e0a5b8f18130fd1dacb42dfa292227f9ef4784fc807756e | 2 | 620 |
"""Build a validation-selected ensemble from saved MLP checkpoints.
This is intentionally simple: score available checkpoints, average probability
outputs, and keep the ensemble only if validation macro AP improves over the best
single MLP. The final report uses the resulting predictions.
"""
from __future__ import ... | mlpc_task_4 | src/ensemble_mlp.py | Python | 02e7e983a027f3273db733a9ee88d68a2b3eec84e5c22c8220e60a04dd6d4a13 | 0 | 896 |
candidate in candidates], axis=0).astype(np.float32)
return val_scores, test_scores, macro_ap(y_val, val_scores), micro_ap(y_val, val_scores)
def _search_ensembles(
candidates: list[CandidateScores],
y_val: np.ndarray,
max_pool: int = 14,
) -> tuple[list[dict[str, Any]], list[CandidateScores], np.ndar... | mlpc_task_4 | src/ensemble_mlp.py | Python | 3bb04f9729eeffa96e2a577ff8f8e075897b3adcc20cc092273dc9f4b73610ff | 1 | 896 |
"mlp_val_scores"])
def _update_predictions(
val_scores: np.ndarray,
test_scores: np.ndarray,
y_val: np.ndarray,
y_test: np.ndarray,
val_idx: np.ndarray,
test_idx: np.ndarray,
class_names: np.ndarray,
) -> None:
existing: dict[str, np.ndarray] = {}
if config.PREDICTIONS_TEST.exists(... | mlpc_task_4 | src/ensemble_mlp.py | Python | 47baf3e462c2f49b612b3f3a85640487f6d9c1e725d8a1e072998853308e6ce8 | 2 | 684 |
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from . import config
from .metrics import macro_ap, micro_ap, per_class_ap
def _metric_row(
name: str, ... | mlpc_task_4 | src/final_eval.py | Python | d01e76d1d17e47c0dbf743ed556d4ecce019c4b6de0ea7521dcac76422c2f530 | 0 | 896 |
asarray(file_ids).astype(str) == str(file_id)
if not mask.any():
raise ValueError(f"file_id {file_id!r} not found")
truth = np.asarray(y_true)[mask].T
score = np.asarray(y_score)[mask].T
x = np.asarray(times)[mask] if times is not None else np.arange(score.shape[1])
output = (
Path... | mlpc_task_4 | src/final_eval.py | Python | bf2f46500e578f8609d198cb258426db5fd7fe35e46f1e56ac0e5443339b36a3 | 1 | 580 |
from __future__ import annotations
import numpy as np
from sklearn.metrics import average_precision_score, f1_score
def _validate_targets(y_true: np.ndarray, y_score: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
true = np.asarray(y_true)
score = np.asarray(y_score, dtype=np.float32)
if true.shape != sco... | mlpc_task_4 | src/metrics.py | Python | 52c58fe8f6c847166f2ca74e7ecff2f4e7a9240a691611a9f03aca52ea9e022b | 0 | 666 |
from __future__ import annotations
from collections.abc import Mapping
from pathlib import Path
import joblib
import numpy as np
from sklearn.preprocessing import StandardScaler
from . import config
from .splits import load_dataset_cache
def fit_scaler(x_train: np.ndarray) -> StandardScaler:
scaler = StandardS... | mlpc_task_4 | src/preprocess.py | Python | dfa029cb90a0f1e552019060cef3c9b2bc9ddefa7afded4392772953a47da783 | 0 | 896 |
else:
if file_order is None:
raise ValueError("file_order is required when per_file_iou is an array")
values = np.asarray(per_file_iou, dtype=np.float32)
lookup = {str(file_id): float(values[i, class_idx]) for i, file_id in enumerate(file_order)}
return np.asarray(
[
... | mlpc_task_4 | src/preprocess.py | Python | ab8de4ea7b133bf117dd3a72a176db0a6bc6254bd1b9728f5658569db2086028 | 1 | 388 |
"""Focused CUDA logistic-regression sweep used for the final report.
The standard sklearn one-vs-rest model is kept in ``train_lr.py``. This module
is an optional experiment runner: it trains a linear sigmoid head with PyTorch so
the same temporal-context features and class-weighted BCE setup can be compared
against ... | mlpc_task_4 | src/refine_lr_torch.py | Python | df7fde681308178dc44d63ef66f8400ac3ade6b4868ae883b0db34a92ada05ea | 0 | 896 |
.read_csv(path)
if frame.empty or "macro_ap" not in frame:
return -np.inf
return float(frame["macro_ap"].max())
def _predict_proba(model: TorchLogisticRegression, features: np.ndarray, batch_size: int = 16384) -> np.ndarray:
model.eval()
device = next(model.parameters()).device
x = np.asar... | mlpc_task_4 | src/refine_lr_torch.py | Python | 5cdd04c5d0877b93b9c3f6d700b0a49bfa47e1cffffdf9503645ab8d01937775 | 1 | 896 |
(np.mean(losses)), "val_macro_ap": val_macro}
)
if val_macro > best_macro:
best_macro = val_macro
best_scores = val_scores
best_state = {
key: value.detach().cpu().clone() for key, value in model.state_dict().items()
}
bad_epoch... | mlpc_task_4 | src/refine_lr_torch.py | Python | fade8811df006b27da6a6bce6daa279c93e89cf4a28674662648a57876c90411 | 2 | 896 |
None
rows: list[dict[str, Any]] = []
candidate_dir = config.RESULTS_DIR / "lr_refine_candidates"
candidate_dir.mkdir(parents=True, exist_ok=True)
print(f"incumbent_lr_val_macro={incumbent:.6f}")
start = time.time()
for run_idx, params in enumerate(CANDIDATES, start=1):
feature_key = str... | mlpc_task_4 | src/refine_lr_torch.py | Python | baf73dd1246a096a60af2006584f5849bf98e41c27d51f0c3a10eb38f1642afc | 3 | 680 |
"""Focused MLP refinement sweep used after the broad baseline sweep.
The broad, reusable MLP trainer lives in ``train_mlp.py``. This module keeps the
small hand-selected refinement grid that was run for the submitted Task 4
results and promotes a candidate only when validation macro AP improves.
"""
from __future__ ... | mlpc_task_4 | src/refine_mlp.py | Python | 3a69bac61d62421845a977013451d3ac295bbb4b58288eba2626ec36938c5e69 | 0 | 896 |
": float(metrics["runtime_s"]),
"epochs": int(metrics["epochs"]),
}
row.update(
{
f"ap_{name}": float(value)
for name, value in zip(class_names, per_class_ap(y_true, y_score), strict=True)
}
)
return row
def _merge_sweeps(refine_frame: pd.DataFrame) -> N... | mlpc_task_4 | src/refine_mlp.py | Python | a469d4aec35cab51bed3cc190922d1d3cd644655ecb12f8591028bd69bd0446e | 1 | 896 |
seed"],
model_path=candidate_path,
)
rows.append(_row(params, metrics, y[val_idx], val_scores, class_names))
print(
f" val_macro={metrics['macro_ap']:.6f} "
f"val_micro={metrics['micro_ap']:.6f} epochs={metrics['epochs']} "
f"runtime_s={metrics['r... | mlpc_task_4 | src/refine_mlp.py | Python | 6178cd6a6e7216964a6b362a8dc0a6cf761760d5af89b290d9bd51ef6e2c8857 | 2 | 439 |
"""Generate report figures from result summaries and cached predictions."""
from __future__ import annotations
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from . import config
from .final_eval import _file_f1
REPORT_FIG_DIR = config.ROOT / "report... | mlpc_task_4 | src/report_assets.py | Python | 8428f757e06d4d6cd8793ced8eedd438b5c48402c901128cc57c317ec9ead375 | 0 | 896 |
.close(fig)
def make_per_class_ap_figure() -> None:
REPORT_FIG_DIR.mkdir(parents=True, exist_ok=True)
frame = pd.read_csv(config.FINAL_TABLE_CSV)
classes = [col[3:] for col in frame.columns if col.startswith("ap_")]
mlp = frame[frame["model"] == "mlp"].iloc[0]
order = sorted(classes, key=lambda na... | mlpc_task_4 | src/report_assets.py | Python | 1f09b8ac0e3afb405086d187d9ac14d72e9565bbb4ea77b622caaef12dfd9ceb | 1 | 896 |
[:7]
labels = [_short_label(str(class_names[idx])) for idx in active]
axes[0].imshow(mel.T, origin="lower", aspect="auto", cmap="magma")
axes[0].set_title(title, fontsize=10)
axes[0].set_ylabel("mel bin")
axes[0].set_xticks([])
axes[1].imshow(y_file[:, active].T, origin="lower", aspect="auto",... | mlpc_task_4 | src/report_assets.py | Python | 71454b94ef4c0a5e1b7c02856f1add59af2eb624bb9c7d9e39c7aaa153127f00 | 2 | 764 |
from __future__ import annotations
import argparse
from pathlib import Path
import pandas as pd
from .final_eval import run_final_evaluation
from .train_lr import plot_lr_sweep, sweep_lr
from .train_mlp import sweep_mlp
MLP_GRID = {
"hidden_dims": [
[128],
[256],
[512],
[256, 128... | mlpc_task_4 | src/run_best_sweeps.py | Python | 030b4688896f1db3ac22cd2c426161e7be981b9b44d5e6282de535e834935139 | 0 | 549 |
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