pair programming

[wmilkowska]

No description provided.

[wmilkowska]

an example plot here ^

speed.py contains the easy task, we tried to make the code as clear as possible. I'll be working on other tasks in the near future :)

[roszcz]

Suggested change

	x = df.iloc[0:16]
	x = x[["pitch", "velocity"]]
	scores = {"score": [], "idx": []}
	seq_len = len(x)
	for i in range(0, len(df) - seq_len):
	seq = df.iloc[i : i + seq_len]
	seq = seq[["pitch", "velocity"]]
	score = cos_sim_score(x, seq)
	scores["score"].append(score)
	scores["idx"].append(i)
	similarity = pd.DataFrame(scores)
	similarity.sort_values(by="score", ascending=False, inplace=True)
	print(similarity)
	if __name__ == "__main__":
	# Moved this from the top of the script
	dataset = load_dataset("roszcz/internship-midi-data-science", split="train")
	record = dataset[0]
	df = pd.DataFrame(record["notes"])
	print(df.head())
	x = df.iloc[0:16]
	x = x[["pitch", "velocity"]]
	scores = {"score": [], "idx": []}
	seq_len = len(x)
	for i in range(0, len(df) - seq_len):
	seq = df.iloc[i : i + seq_len]
	seq = seq[["pitch", "velocity"]]
	score = cos_sim_score(x, seq)
	scores["score"].append(score)
	scores["idx"].append(i)
	similarity = pd.DataFrame(scores)
	similarity.sort_values(by="score", ascending=False, inplace=True)
	print(similarity)

Having code outside of if __name__ == "__main__" will execute it every time you try to import this file (i.e. from sequence_similarity import cos_sim_score), and it's usually better to avoid that :)

[roszcz]

Suggested change

	def cos_sim_score(sequence: pd.DataFrame, window: pd.DataFrame) -> float:
	"""
	Calculating cosine similarity between sequence and window
	Args:
	sequence (pd.DataFrame): input sequence
	window (pd.DataFrame): subset of rolling window
	Returns:
	float: cosine similarity score
	"""
	def cos_sim_score(sequence_a: pd.DataFrame, sequence_b: pd.DataFrame) -> float:
	"""
	Calculating cosine similarity between two sequences
	Args:
	sequence_a (pd.DataFrame): first sequence
	sequence_b (pd.DataFrame): second sequence
	Returns:
	float: cosine similarity score
	"""

This function is great, but name "window" doesn't really make sense from the point of view of this function - it measures distance between any two sequences and it's only in your specific use case that the second sequence is a "window" moving over the signal.

[roszcz]

Suggested change

	x = df.iloc[0:16]
	x = x[["pitch", "velocity"]]
	scores = {"score": [], "idx": []}
	seq_len = len(x)
	for i in range(0, len(df) - seq_len):
	seq = df.iloc[i : i + seq_len]
	seq = seq[["pitch", "velocity"]]
	score = cos_sim_score(x, seq)
	scores["score"].append(score)
	scores["idx"].append(i)
	target_sequence = df.iloc[0:16]
	target_sequence = target_sequence[["pitch", "velocity"]]
	scores = {"score": [], "idx": []}
	seq_len = len(target_sequence)
	for i in range(0, len(df) - seq_len):
	sequence_window = df.iloc[i : i + seq_len]
	sequence_window = sequence_window[["pitch", "velocity"]]
	score = cos_sim_score(sequence_a=target_sequence, sequence_b=sequence_window)
	scores["score"].append(score)
	scores["idx"].append(i)

Logic's good, these are just readability suggestions 👍